[{"data":1,"prerenderedAt":800},["ShallowReactive",2],{"/en-us/blog/duo-agent-platform-with-mcp":3,"navigation-en-us":38,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Itzik Gan Baruch":700,"blog-related-posts-en-us-duo-agent-platform-with-mcp":714,"assessment-promotions-en-us":753,"next-steps-en-us":790},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":28,"isFeatured":12,"meta":29,"navigation":30,"path":31,"publishedDate":20,"seo":32,"stem":34,"tagSlugs":35,"__hash__":37},"blogPosts/en-us/blog/duo-agent-platform-with-mcp.yml","Duo Agent Platform With Mcp",[7],"itzik-gan-baruch",null,"ai-ml",{"slug":11,"featured":12,"template":13},"duo-agent-platform-with-mcp",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"updatedDate":21,"category":9,"tags":22,"body":27},"Model Context Protocol integration","Extend GitLab Duo with external Services using MCP. Connect to Jira, Slack, AWS, and more as an MCP client, or enable external AI tools to access your GitLab data as an MCP server.",[18],"Itzik Gan Baruch","https://res.cloudinary.com/about-gitlab-com/image/upload/v1765809212/noh0mdfn9o94ry9ykura.png","2025-09-26","2026-01-14",[23,24,25,26],"AI/ML","product","features","tutorial","*Welcome to Part 7 of our eight-part guide, [Getting started with GitLab Duo Agent Platform](/blog/gitlab-duo-agent-platform-complete-getting-started-guide/), where you'll master building and deploying AI agents and workflows within your development lifecycle. Follow tutorials that take you from your first interaction to production-ready automation workflows with full customization.*\n\n**In this article:**\n- [What is Model Context Protocol (MCP)](#what-is-mcp)\n- [GitLab as MCP client (connect to external services)](#setting-up-gitlab-mcp-client)\n- [GitLab as MCP server (external AI tools access GitLab)](#gitlab-mcp-server-capabilities)\n- [Setup and configuration](#how-to-configure-mcp-server-in-your-ai-tool)\n- [Real-world examples](#using-the-mcp-server)\n\nAI can accelerate development by generating code, debugging, and automating routine tasks. But on its own, it's limited to trained data or public sources, while developers often need access to internal systems like project trackers, dashboards, databases, design files in Figma, or documents in Google Drive.\nNow integrated into [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/), the Model Context Protocol ([MCP](https://about.gitlab.com/topics/ai/model-context-protocol/)) gives AI secure access to internal services so developers can get comprehensive assistance directly within their workflows.\n\n> 🎯 Try [**GitLab Duo Agent Platform**](https://about.gitlab.com/gitlab-duo-agent-platform/) today!\n## What is MCP?\nMCP, first introduced by Anthropic in 2024, is an open standard that connects AI with data and tools. It works as a secure two-way channel: MCP clients (AI applications, autonomous agents, or development tools) request data or actions, and MCP servers provide trusted, authorized responses from their connected data sources.\nMCP servers act as secure bridges to various systems: They can connect to databases, APIs, file systems, cloud services, or any external service to retrieve and provide data. This enables AI tools and agents to go beyond their initial training data by allowing them to access real-time information and execute actions, such as rescheduling meetings or checking calendar availability, while maintaining strict security, privacy, and audit controls.\n## Developer-focused MCP examples\nDevelopers unlock powerful capabilities when connecting MCP to their development tools and workflows. Here are practical examples of what AI can do with MCP servers in a development context:\n\n- Review open issues and create merge requests\n- Retrieve deployment logs and error traces\n- Check team communication in Slack about technical decisions\n- Reschedule meetings or checking calendar availability for team coordination.\n\nThese developer-focused capabilities enable AI to provide meaningful assistance directly within developers' workflows, without requiring context-switching between tools.\n\n## Why use MCP?\nYou may ask: Why use MCP if AI can already call system APIs directly? The challenge is that each API has its own authentication, data formats, and behaviors, which would require AI to use custom connectors for every system and continuously maintain them as APIs evolve, making direct integrations complex and error-prone. MCP addresses this by providing a standardized, secure interface that handles authentication, permissions, and data translation. This enables AI tools to connect reliably to any system, while simplifying integration and ensuring consistent, safe behavior.\n## GitLab's MCP support\nGitLab extends [Duo Agentic Chat](https://about.gitlab.com/blog/gitlab-duo-chat-gets-agentic-ai-makeover/) with MCP support, shattering the barriers that previously isolated AI from the tools developers use every day. This empowers developers to access their entire toolkit directly from their favorite IDE, in natural language, enabling GitLab Duo Agent Platform to deliver comprehensive assistance without breaking developer flow or forcing disruptive context switches.\nGitLab provides comprehensive MCP support through two complementary workflows:\n-  **[MCP client workflow](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/):** Duo Agent Platform serves as an MCP client, allowing features to access various external tools and services.\n- **[MCP server workflow](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_server/):** GitLab also provides MCP server capabilities, enabling AI tools and applications like Claude Desktop, Cursor, and other MCP-compatible tools to connect securely to your GitLab instance.\n## Interactive walkthrough demo of the MCP client workflow\n**Picture this common Monday morning scenario:** Your company's checkout service is throwing timeout errors. Customers can't complete purchases, and you need to investigate fast. Normally, you'd open Jira to review the incident ticket, scroll through Slack for updates, and check Grafana dashboards for error spikes. With GitLab's MCP support, you can do all of this in natural language directly from the chat in your IDE. MCP correlates data across all your systems, giving you the full picture instantly, without leaving your development workflow.\nTo experience this capability firsthand, we've created an [interactive walkthrough](https://gitlab.navattic.com/mcp) illustrating the payment service scenario above. Click the image below to start the demo.\n\n[![MCP walkthrough](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758206468/osf0wkwe1l45oc6zjdhr.png)](https://gitlab.navattic.com/mcp)\n\n## Setting up GitLab MCP client\nBefore you can start querying data through [GitLab Duo Agentic Chat](https://docs.gitlab.com/user/gitlab_duo_chat/agentic_chat/) or the [software development flow](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/software_development/), you need to configure MCP in your development environment. The steps include:\n- **Turn on Feature preview** — In your Group settings, navigate to **GitLab Duo** in the left sidebar, then check the box for \"Turn on experiment and beta GitLab Duo features\" under the **Feature preview** section.\n- **Turn on MCP for your group** — Enable MCP support in your GitLab group settings to allow Duo features to connect to external systems.\n- **Set up MCP servers** — Define the MCP servers in JSON format in the `mcp.json` file. Create the file in this location:\n\n    - **Windows:** `C:\\Users\\\u003Cusername>\\AppData\\Roaming\\GitLab\\duo\\mcp.json`\n    - **All other operating systems:** `~/.gitlab/duo/mcp.json`\n\nFor workspace-specific configurations, see [workspace configuration setup](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/#create-workspace-configuration).\n```json\n{\n  \"mcpServers\": {\n    \"server-name\": {\n      \"type\": \"stdio\",\n      \"command\": \"path/to/server\",\n      \"args\": [\"--arg1\", \"value1\"],\n      \"env\": {\n        \"ENV_VAR\": \"value\"\n      }\n    },\n    \"http-server\": {\n      \"type\": \"http\",\n      \"url\": \"http://localhost:3000/mcp\"\n    },\n    \"sse-server\": {\n      \"type\": \"sse\",\n      \"url\": \"http://localhost:3000/mcp/sse\"\n    }\n  }\n}\n```\n- **Install and configure your IDE** — Ensure VSCodium or Visual Studio Code is installed along with the GitLab Workflow extension (Version 6.28.2 or later for basic MCP support, 6.35.6 or later for full support).\nFor full step-by-step instructions, configuration examples, and troubleshooting tips, see the [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/).\n## Example project\nTo complement the walkthrough, we are sharing the [project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/mcp/gitlab-duo-mcp-demo.git) that served as its **foundation**. This project allows you to reproduce the same flow in your own environment and explore GitLab's MCP capabilities hands-on.\nIt demonstrates MCP functionality in a simulated enterprise setup, using mock data from Jira, Slack, and Grafana to model an incident response scenario. The included `mcp.json` configuration shows how to connect to a local MCP server (`enterprise-data-v2`) or optionally extend the setup with AWS services for cloud integration.\n```json\n{\n  \"mcpServers\": {\n    \"enterprise-data-v2\": {\n      \"type\": \"stdio\",\n      \"command\": \"node\",\n      \"args\": [\"src/server.js\"],\n      \"cwd\": \"/path/to/your/project\"\n    },\n    \"aws-knowledge\": {\n      \"type\": \"stdio\"\n      \"command\": \"npx\",\n      \"args\": [\"mcp-remote\", \"https://knowledge-mcp.global.api.aws\"]\n    },\n    \"aws-console\": {\n      \"type\": \"stdio\"\n      \"command\": \"npx\",\n      \"args\": [\"@imazhar101/mcp-aws-server\"],\n      \"env\": {\n        \"AWS_REGION\": \"YOUR_REGION\",\n        \"AWS_PROFILE\": \"default\"\n      }\n    }\n  }\n}\n```\n\n**Security note:** The `aws-console` uses a community-developed MCP server package (`@imazhar101/mcp-aws-server`) for AWS integration that has not been independently verified. This is intended for demonstration and learning purposes only. For production use, evaluate packages thoroughly or use official alternatives.\n\nAdditionally, configure AWS credentials using AWS CLI profiles or IAM roles rather than hardcoding them in the configuration file. The AWS SDK will automatically discover credentials from your environment, which is the recommended approach for enterprise governance and security compliance.\n\nTo get started, [clone the project](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/mcp/gitlab-duo-mcp-demo.git), install dependencies with `npm install`, then start the local MCP server with `npm start`. Create an `~/.gitlab/duo/mcp.json` file with the configuration above, update the file path to match your local setup, and restart VS Code to load the MCP configuration. Optionally, add your AWS credentials to experience live cloud integration.\n\nClone the project here: [GitLab Duo MCP Demo](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-agent-platform/mcp/gitlab-duo-mcp-demo.git).\n\n## Example prompts to try with the demo project\nOnce you've configured the example project, you can start exploring your data and tools directly from GitLab Duo Agentic Chat in your IDE. Here are some prompts you can try:\n- \"What tools can you access through MCP?\"\n\n\n![What tools can you access through MCP?](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203432/xmahjenvoa82ov3kttqx.png)\n\n- \"Show me recent Slack discussions about the database issues.\"\n\n![Slack discussion about tools to access through MCP](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203432/wdwp5xzq6umeanb1xwbq.png)\n\n## GitLab MCP server capabilities\nSo far, we've looked at how GitLab Duo Agent Platform acts as an MCP client, connecting to external MCP servers. Now, let's explore the GitLab MCP server capabilities.\nThe GitLab MCP server lets AI tools like Cursor or Claude Desktop connect securely to your GitLab instance and work with your development data through natural language. Authentication is handled through OAuth 2.0 Dynamic Client Registration, so AI tools can register automatically and access your GitLab data with proper authorization.\nCurrently, the server supports:\n  - **Issues** — get details or create new issues\n  - **Merge requests** — view details, commits, and file changes\n  - **Pipelines** — list jobs and pipelines for merge requests\n  - **Server info** — check the MCP server version\n\nFor the complete list of available tools and capabilities, see the [MCP server docs](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_server/#available-tools-and-capabilities).\n## Interactive walkthrough: GitLab MCP server in action\nExperience the GitLab MCP server firsthand with our [interactive walkthrough](https://gitlab.navattic.com/gitlab-mcp-server).\nIt guides you through setting up Cursor with the MCP server and using Cursor Chat to securely connect to your GitLab instance. You'll see how to perform actions like viewing issues, creating a new issue, and checking merge requests, all directly through natural language, without leaving your development environment.\n\n[![MCP server walkthrough](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203431/y2zdd71miiw0pkwd0a5a.png)](https://gitlab.navattic.com/gitlab-mcp-server)\n### How to configure MCP server in your AI tool\n**Prerequisites:**\n- Ensure **Node.js** and **npm** are installed\n- Verify that `npx` is globally accessible by running `npx --version` in your terminal\n1. **Enable feature flags**\n   - Activate `mcp_server` and `oauth_dynamic_client_registration` in your GitLab instance\n\n2. **Add GitLab MCP server configuration to your AI tool**\n   - Add the MCP server entry to your tool's configuration file (`mcp.json` for Cursor, `claude_desktop_config.json` for Claude Desktop):\n\n  ```json\n  {\n    \"mcpServers\": {\n      \"GitLab\": {\n        \"command\": \"npx\",\n        \"args\": [\n          \"mcp-remote\",\n          \"https://\u003Cyour-gitlab-instance>/api/v4/mcp\",\n          \"--static-oauth-client-metadata\",\n          \"{\\\"scope\\\": \\\"mcp\\\"}\"\n        ]\n      }\n    }\n  }\n  ```\n\n### Register and authenticate\nOn first connection, the AI tool will:\n- Automatically register as an OAuth application\n- Request authorization for the mcp scope\n### Authorize in browser\nWhen connecting, the MCP client will automatically open your default browser to complete the OAuth flow. Review and approve the request in GitLab to grant access and receive an access token for secure API access.\n\n![Access request](https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203431/szkjoqkdxstdbdh4eirv.png)\n\n### Using the MCP server\nOnce your AI tool is connected to the MCP server, you can securely fetch and act on GitLab data (issues, merge requests, and pipelines) directly from your development environment using natural language. For example:\n\n- `Get details for issue 42 in project 123`\n- `Create a new issue titled \"Fix login bug\" with description about password special characters`\n- `Show me all commits in merge request 15 from the gitlab-org/gitlab project`\n- `What files were changed in merge request 25?`\n- `Show me all jobs in pipeline 12345`\n\n> This feature is experimental, controlled by a feature flag, and not yet ready for production use.\nFor full step-by-step instructions, configuration examples, and troubleshooting tips, see the [GitLab MCP server documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_server/).\n## Summary\n\nGitLab Duo Agent Platform supports MCP, enabling AI-powered development workflows with external tool integration. With MCP support, GitLab acts as both a client and a server:\n- **MCP Client:** GitLab Duo Agent Platform can securely access data and services from external systems, bringing rich context directly into the IDE.\n- **MCP server:** External AI tools like Cursor or Claude Desktop can connect to your GitLab instance, access project data, and perform actions, all while maintaining strict security and privacy.\nThis bidirectional support reduces context switching, accelerates developer workflows, and ensures AI can provide meaningful assistance across your entire toolkit.\n\n## What's next?\n\nYou now understand how to use agents, create flows, discover solutions in the AI Catalog, manage workflows through the Automate menu, and extend capabilities with MCP. The final step is customizing GitLab Duo to match your team's specific needs. Learn this in [Part 8](/blog/customizing-gitlab-duo-chat-rules-prompts-workflows/), including how to create custom chat rules, craft effective system prompts, configure agent tools, set up MCP integrations, and tailor flows for your team's unique workflow.\n\n## Resources\n\n- [MCP Clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/)\n- [MCP Server documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_server/)\n- [What is Model Context Protocol?](https://about.gitlab.com/topics/ai/model-context-protocol/)\n- [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/)\n\n---\n**Next:** [Part 8: Customizing GitLab Duo: Chat rules, prompts, and workflows](/blog/customizing-gitlab-duo-chat-rules-prompts-workflows/)\n\n**Previous:** [Part 6: Monitor, manage, and automate AI 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GitLab Duo Agent Platform: Connect any tool with MCP","Learn how to connect external tools to GitLab Duo Agent Platform using MCP. Step-by-step setup with three practical workflow demos.",[721],"Albert Rabassa","2026-03-05",[9,24,26],"Managing software development often means juggling multiple tools: tracking issues in Jira, writing code in your IDE, and collaborating through GitLab. Context switching between these platforms disrupts focus and slows down delivery.\n\nWith GitLab Duo Agent Platform's [MCP](https://about.gitlab.com/topics/ai/model-context-protocol/) support, you can now connect Jira or any tool that supports MCP directly to your AI-powered development environment. Query issues, update tickets, and sync your workflow — all through natural language, without ever leaving your IDE.\n\n## What you'll learn\n\nIn this tutorial, we'll walk you through:\n\n* **Setting up the Jira/Atlassian OAuth application** for secure authentication\n* **Configuring GitLab Duo Agent Platform** as an MCP client\n* **Three practical use cases** demonstrating real-world workflows\n\n## Prerequisites\n\nBefore getting started, ensure you have the following:\n\n| Requirement | Details |\n| ---- | ----- |\n| **GitLab instance** | GitLab 18.8+ with Duo Agent Platform enabled |\n| **Jira account** | Jira Cloud instance with admin access to create OAuth applications |\n| **IDE** | Visual Studio Code with GitLab Workflow extension installed |\n| **MCP support** | MCP support enabled in GitLab |\n\n\n## Understanding the architecture\n\nGitLab Duo Agent Platform acts as an **MCP client**, connecting to the Atlassian MCP server to access your Jira project management data. Atlassian  MCP server handles authentication, translates natural language requests into API calls, and returns structured data back to GitLab Duo Agent Platform — all while maintaining security and audit controls.\n\n## Part 1: Configure Jira OAuth application\n\nTo securely connect GitLab Duo Agent Platform to your Jira instance, you'll need to create an OAuth 2.0 application in the Atlassian Developer Console. This grants to GitLab the MCP server authorized access to your Jira data.\n\n### Setup steps\n\nIf you prefer to configure manually, follow these steps:\n\n1. **Navigate to the Atlassian Developer Console**\n\n   * Go to [developer.atlassian.com/console/myapps](https://developer.atlassian.com/console/myapps)\n\n   * Sign in with your Atlassian account\n\n2. **Create a new OAuth 2.0 app**\n\n   * Click **Create** → **OAuth 2.0 integration**\n\n   * Enter a name (e.g., \"gitlab-dap-mcp\")\n\n   * Accept the terms and click **Create**\n\n3. **Configure permissions**\n\n   * Navigate to **Permissions** in the left sidebar.\n\n   * Add **Jira API** and configure the following scopes:\n\n     * `read:jira-work` — Read issues, projects, and boards\n\n     * `write:jira-work` — Create and update issues\n\n     * `read:jira-user` — Read user information\n\n4. **Set up authorization**\n\n   * Go to **Authorization** in the left sidebar\n\n   * Add a callback URL for your environment (`https://gitlab.com/oauth/callback`)\n\n   * Save your changes\n\n5. **Retrieve credentials**\n\n   * Navigate to **Settings**\n\n   * Copy your **Client ID** and **Client Secret**\n\n   * Store these securely — you'll need them for the MCP configuration\n\n\n### Interactive walkthrough: Jira OAuth setup\n\nClick on the image below to get started.\n\n\n[![Jira OAuth setup tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772644850/wnzfoq43nkkfmgdqldmr.png)](https://gitlab.navattic.com/jira-oauth-setup)\n\n\n## Part 2: Configure GitLab Duo Agent Platform MCP client\n\nWith your OAuth credentials ready, you can now configure GitLab Duo Agent Platform to connect to the Atlassian MCP server.\n\n### Create your MCP configuration file\n\nCreate the MCP configuration file in your GitLab project at `.gitlab/duo/mcp.json`:\n\n\n```json\n{\n  \"mcpServers\": {\n    \"atlassian\": {\n      \"type\": \"http\",\n      \"url\": \"https://mcp.atlassian.com/v1/mcp\",\n      \"auth\": {\n        \"type\": \"oauth2\",\n        \"clientId\": \"YOUR_CLIENT_ID\",\n        \"clientSecret\": \"YOUR_CLIENT_SECRET\",\n        \"authorizationUrl\": \"https://auth.atlassian.com/oauth/authorize\",\n        \"tokenUrl\": \"https://auth.atlassian.com/oauth/token\"\n      },\n      \"approvedTools\": true\n    }\n  }\n}\n```\n\nReplace `YOUR_CLIENT_ID` and `YOUR_CLIENT_SECRET` with the credentials you generated in Part 1.\n\n### Enable MCP in GitLab\n\n1. Navigate to your **Group Settings** → **GitLab Duo** → **Configuration**\n2. Make sure “Allow external MCP tools” is checked\n\n### Verify the connection\n\nOpen your project in VS Code and ask in GitLab Duo Agent Platform chat:\n\n```text\nWhat MCP tools do you have access to?\n```\n\nThen\n\n```text\nTest the MCP JIRA configuration in this project\n```\n\nAt this point you'll be redirected from the IDE to the MCP Atlassian website to approve access:\n\n![Redirect to MCP Atlassian website](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/z5acqjgguh0damnnde9g.png \"Redirect to MCP Atlassian website\")\n\n\u003Cbr>\u003C/br>\n\n![Approve access](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/rwowamm8nsubhpixtn3i.png \"Approve access\")\n\n\u003Cbr>\u003C/br>\n\n![Select your JIRA instance and approve](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/chuzqd0jeptfwvoj7wjr.png \"Select your JIRA instance and approve\")\n\n\u003Cbr>\u003C/br>\n\n![Success!](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/bsgti5iste2bzck19o5y.png \"Success!\")\n\n\u003Cbr>\u003C/br>\n\n### Verify with the MCP Dashboard\n\nGitLab also provides a built-in **MCP Dashboard** directly in your IDE for this.\n\nIn VS Code or VSCodium, open the Command Palette (`Cmd+Shift+P` on macOS, `Ctrl+Shift+P` on Windows/Linux) and search for **\"GitLab: Show MCP Dashboard\"**. The dashboard opens in a new editor tab and gives you:\n\n* **Connection status** for each configured MCP server\n* **Available tools** exposed by the server (e.g., `jira_get_issue`, `jira_create_issue`)\n* **Server logs** so you can see exactly which tools are being called in real time\n\n![MCP servers dashboard and status](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/mmvdfchucacsydivowvn.png \"MCP servers dashboard and status\")\n\n\u003Cbr>\u003C/br>\n\n![Server details and permissions](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/tcocgdvovp2dl42pvfn8.png \"Server details and permissions\")\n\n\u003Cbr>\u003C/br>\n\n\n![MCP Server logs](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643466/mougvqqk1bozchaufsci.png \"MCP Server logs\")\n\n\u003Cbr>\u003C/br>\n\n### Interactive walkthrough: Testing MCP\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005495?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Testing MCP\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Part 3: Use cases in action\n\nNow that your integration is configured, let's explore three practical workflows that demonstrate the power of connecting Jira to GitLab Duo Agent Platform.\n\n### Planning assistant\n\n**Scenario:** You're preparing for sprint planning and need to quickly assess the backlog, understand priorities, and identify blockers.\n\nThis demo shows you how to:\n\n* Query the backlog\n* Identify unassigned high-priority issues\n* Get AI-powered sprint recommendations\n\n#### Example prompts\n\nTry these prompts in GitLab Duo Agent Platform Chat:\n\n```text\nList all the unassigned issues in JIRA for project GITLAB\n```\n\n```text\nSuggest the two top issues to prioritize and summarize them. Assign them to me.\n```\n\n### Interactive walkthrough: Project planning\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005462?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Project Planning\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player. js\">\u003C/script>\n\n### Issue triage and creation from code\n\n**Scenario:** While reviewing code, you discover a bug and want to create a Jira issue with relevant context — without leaving your IDE.\n\nThis demo walks you through:\n\n* Identifying a bug while coding\n* Creating a detailed Jira issue via natural language\n* Auto-populating issue fields with code context\n* Linking the issue to your current branch\n\n#### Example prompts\n\n```text\nSearch in JIRA for a bug related to: Null pointer exception in PaymentService.processRefund().\nIf it does not exist create it with all the context needed from the code. Find possible blockers that this bug may cause.\n```\n\n```text\nCreate a new branch called issue-gitlab-18, checkout, and link it to the issue we just created. Assign the JIRA issue to me and mark it as in-progress.\n```\n\n### Interactive walkthrough: Bug review and task automation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005368?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Bug Review\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n### Cross-system incident investigation\n\n**Scenario:** A production incident occurs, and you need to correlate information from Jira (incident ticket), GitLab Project Management, your codebase, and merge requests to identify the root cause.\n\nThis demo demonstrates:\n\n* Fetching incident details from Jira\n* Correlating with recent merge requests in GitLab\n* Identifying potentially related code changes\n* Generating an incident timeline\n* Design a remediation plan and create it as a work item in GitLab\n\n#### Example prompts\n\n```text\n\"We have a production incident INC-1 about checkout failures. Can you help me investigate with all available context?\"\n```\n\n```text\nCreate a timeline of events for incident INC-1 including related Jira issues and recent deployments\n```\n\n```text\nPropose a remediation plan\n```\n\n### Interactive walkthrough: Cross-system troubleshooting and remediation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005413?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Cross System Investigation\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Troubleshooting\n\nThese are some common setup issues and quick fixes:\n\n| Issue | Solution |\n| ----- | ----- |\n| \"MCP server not found\" | Verify the `mcp.json` file is in the correct location and properly formatted |\n| \"Authentication failed\" | Re-check your OAuth credentials and ensure scopes are correctly configured in Atlassian |\n| \"No Jira tools available\" | Restart VS Code after updating `mcp.json` and ensure MCP is enabled in GitLab |\n| \"Connection timeout\" | Check your network connectivity to `mcp.atlassian.com` |\n\n\u003Cbr/> For detailed troubleshooting, see the [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/).\n\n\n## Security considerations\n\nWhen integrating Jira with GitLab Duo Agent Platform:\n\n* **OAuth tokens** — Make sure credentials remain secure\n* **Principle of least privilege** — Only grant the minimum required Jira scopes\n* **Token rotation** — Regularly rotate your OAuth credentials as part of security hygiene\n\n\n## Summary\n\nConnecting GitLab Duo Agent Platform to different tools through MCP transforms how you interact with your development lifecycle. In this article, you have learned how to:\n\n* **Query issues naturally** — Ask questions about your backlog, sprints, and incidents in natural language.\n* **Create and update issues on all your DevSecOps environment** — File bugs and update tickets without leaving your IDE.\n* **Correlate across systems** — Combine Jira data with GitLab project management, merge requests, and pipelines for complete visibility.\n* **Reduce context switching** — Keep your focus on code while staying connected to project management.\n\nThis integration exemplifies the power of MCP: standardized, secure access to your tools through AI, enabling developers to work more efficiently without sacrificing governance or security.\n\n\n## Read more\n\n* [GitLab Duo Agent Platform adds support for Model Context Protocol](https://about.gitlab.com/blog/duo-agent-platform-with-mcp/)\n\n* [What is Model Context Protocol?](https://about.gitlab.com/topics/ai/model-context-protocol/)\n\n* [Agentic AI guides and resources](https://about.gitlab.com/blog/agentic-ai-guides-and-resources/)\n\n* [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/)\n\n* [Get started with GitLab Duo Agent Platform: The complete guide](https://about.gitlab.com/blog/gitlab-duo-agent-platform-complete-getting-started-guide/)",{"featured":12,"template":13,"slug":726},"extend-gitlab-duo-agent-platform-connect-any-tool-with-mcp",{"content":728,"config":738},{"title":729,"description":730,"authors":731,"heroImage":733,"date":734,"body":735,"category":9,"tags":736},"10 AI prompts to speed your team’s software delivery","Eliminate review backlogs, security delays, and coordination overhead with ready-to-use AI prompts covering every stage of the software lifecycle.",[732],"Chandler Gibbons","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772632341/duj8vaznbhtyxxhodb17.png","2026-03-04","AI-assisted coding tools are helping developers generate code faster than ever. So why aren’t teams _shipping_ faster?\n\nBecause coding is only 20% of the software delivery lifecycle, the remaining 80% becomes the bottleneck: code review backlogs grow, security scanning can’t keep pace, documentation falls behind, and manual coordination overhead increases.\n\nThe good news is that the same AI capabilities that accelerate individual coding can eliminate these team-level delays. You just need to apply AI across your entire software lifecycle, not only during the coding phase.\n\nBelow are 10 ready-to-use prompts from the [GitLab Duo Agent Platform Prompt Library](https://about.gitlab.com/gitlab-duo/prompt-library/) that help teams overcome common obstacles to faster software delivery. Each prompt addresses a specific slowdown that emerges when individual productivity increases without corresponding improvements in team processes.\n\n## How do you move code review from bottleneck to accelerator?\nDevelopers generate merge requests faster with AI assistance, but human reviewers can quickly become overwhelmed as code review cycles stretch from hours to days. AI can handle routine review tasks, freeing reviewers to focus on architecture and business logic instead of catching basic logical errors and API contract violations.\n\n### Review MR for logical errors\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nReview this MR for logical errors, edge cases, and potential bugs: [MR URL or paste code]\n```\n\n**Why it helps**: Automated linters catch syntax issues, but logical errors require understanding intent. This prompt catches bugs before human reviewers even look at the code, reducing review cycles from multiple rounds to often just one approval.\n\n### Identify breaking changes in MR\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nDoes this MR introduce any breaking changes?\n\nChanges:\n[PASTE CODE DIFF]\n\nCheck for:\n1. API signature changes\n2. Removed or renamed public methods\n3. Changed return types\n4. Modified database schemas\n5. Breaking configuration changes\n```\n\n**Why it helps**: Breaking changes discovered during deployment can cause rollbacks and incidents. This prompt shifts that discovery left to the MR stage, when fixes are faster and less expensive.\n\n## How can you shift security left without slowing down?\nSecurity scans generate hundreds of findings. Security teams manually triage each one while developers wait for approval to deploy. Most findings are false positives or low-risk issues, but identifying the real threats requires expertise and time. AI can prioritize findings by actual exploitability and auto-remediate common vulnerabilities, allowing security teams to focus on the threats that matter.\n\n### Analyze security scan results\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n\n```text\n@security_analyst Analyze these security scan results:\n\n[PASTE SCAN OUTPUT]\n\nFor each finding:\n1. Assess real risk vs false positive\n2. Explain the vulnerability\n3. Suggest remediation\n4. Prioritize by severity\n```\n\n**Why it helps**: Most security scan findings are false positives or low-risk issues. This prompt helps security teams focus on the findings that actually matter, reducing remediation time from weeks to days.\n\n### Review code for security issues\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n```text\n@security_analyst Review this code for security issues:\n\n[PASTE CODE]\n\nCheck for:\n1. Injection vulnerabilities\n2. Authentication/authorization flaws\n3. Data exposure risks\n4. Insecure dependencies\n5. Cryptographic issues\n```\n\n**Why it helps**: Traditional security reviews happen after code is written. This prompt enables developers to find and fix security issues before creating an MR, eliminating the back and forth that delays deployments.\n\n## How do you keep documentation current as code changes?\nCode changes faster than documentation. Onboarding new developers takes weeks because docs are outdated or missing. Teams know documentation is important, but it always gets deferred when deadlines approach. Automating documentation generation and updates as part of your standard workflow ensures docs stay current without adding manual work.\n\n### Generate release notes from MRs\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nGenerate release notes for these merged MRs:\n[LIST MR URLs or paste titles]\n\nGroup by:\n1. New features\n2. Bug fixes\n3. Performance improvements\n4. Breaking changes\n5. Deprecations\n```\n\n**Why it helps**: Manual release note compilation takes hours and often includes errors or omissions. Automated generation ensures every release has comprehensive notes without adding work to your release process.\n\n### Update documentation after code changes\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nI changed this code:\n\n[PASTE CODE CHANGES]\n\nWhat documentation needs updating? Check:\n1. README files\n2. API documentation\n3. Architecture diagrams\n4. Onboarding guides\n```\n\n**Why it helps**: Documentation drift happens because teams forget which docs need updates after code changes. This prompt makes documentation maintenance part of your development workflow, not a separate task that gets deferred.\n\n## How do you break down planning complexity?\nLarge features get stuck in planning. Teams spend weeks in meetings trying to scope work and identify dependencies. The complexity feels overwhelming, and it's hard to know where to start. AI can systematically decompose complex work into concrete, implementable tasks with clear dependencies and acceptance criteria, transforming weeks of planning into focused implementation.\n\n### Break down epic into issues\n**Complexity**: Intermediate\n\n**Category**: Documentation\n\n**Agent**: Duo Planner\n\n**Prompt from library**:\n\n```text\nBreak down this epic into implementable issues:\n\n[EPIC DESCRIPTION]\n\nConsider:\n1. Technical dependencies\n2. Reasonable issue sizes\n3. Clear acceptance criteria\n4. Logical implementation order\n```\n\n**Why it helps**: This prompt transforms a week of planning meetings into 30 minutes of AI-assisted decomposition followed by team review. Teams start implementation sooner with clearer direction.\n\n## How can you expand test coverage without expanding effort?\nDevelopers are writing code faster, but if testing doesn't keep pace, test coverage decreases and bugs slip through. Writing comprehensive tests manually is time-consuming, and developers often miss edge cases under deadline pressure. Generating tests automatically means developers can review and refine rather than write from scratch, maintaining quality without sacrificing velocity.\n\n### Generate unit tests\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nGenerate unit tests for this function:\n\n[PASTE FUNCTION]\n\nInclude tests for:\n1. Happy path\n2. Edge cases\n3. Error conditions\n4. Boundary values\n5. Invalid inputs\n```\n\n**Why it helps**: Writing tests manually is time consuming, and developers often miss edge cases. This prompt generates thorough test suites in seconds, which developers can review and adjust rather than write from scratch.\n\n### Review test coverage gaps\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nAnalyze test coverage for [MODULE/COMPONENT]:\n\nCurrent coverage: [PERCENTAGE]\n\nIdentify:\n1. Untested functions/methods\n2. Uncovered edge cases\n3. Missing error scenario tests\n4. Integration points without tests\n5. Priority areas to test next\n```\n\n**Why it helps**: This prompt reveals blind spots in your test suite before they cause production incidents. Teams can systematically improve coverage where it matters most.\n\n## How do you reduce mean time to resolution when debugging?\nProduction incidents take hours to diagnose. Developers wade through logs and stack traces while customers experience downtime. Every minute of debugging is a minute of lost productivity and potential revenue. AI can accelerate root cause analysis by parsing complex error messages and suggesting specific fixes, cutting diagnostic time from hours to minutes.\n\n### Debug failing pipeline\n**Complexity**: Beginner\n\n**Category**: Debugging\n\n**Prompt from library**:\n\n```text\nThis pipeline is failing:\n\nJob: [JOB NAME]\nStage: [STAGE]\nError: [PASTE ERROR MESSAGE/LOG]\n\nHelp me:\n1. Identify the root cause\n2. Suggest a fix\n3. Explain why it started failing\n4. Prevent similar issues\n```\n\n**Why it helps**: CI/CD failures block entire teams. This prompt diagnoses failures in seconds instead of the 15-30 minutes developers typically spend investigating, keeping deployment velocity high.\n\n## Moving from individual gains to team acceleration\nThese prompts represent a shift in how teams apply AI to software delivery. Rather than focusing solely on individual developer productivity, they address the coordination, quality, and knowledge-sharing challenges that actually constrain team velocity.\n\nThe [complete prompt library](https://about.gitlab.com/gitlab-duo/prompt-library/) contains more than 100 prompts across all stages of the software lifecycle: planning, development, security, testing, deployment, and operations. Each prompt is tagged by complexity level (Beginner, Intermediate, Advanced) and categorized by use case, making it easy to find the right starting point for your team.\n\nStart with prompts tagged “Beginner” that address your team’s most pressing obstacles. As your team builds confidence, explore intermediate and advanced prompts that enable more sophisticated workflows. The goal is not just faster coding — it's faster, safer, higher-quality software delivery from planning through production.",[23,737],"DevOps platform",{"featured":12,"template":13,"slug":739},"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":741,"config":751},{"title":742,"description":743,"heroImage":744,"authors":745,"date":747,"body":748,"category":9,"tags":749},"AI can detect vulnerabilities, but who governs risk?","AI-assisted vulnerability detection is developing fast, but the harder challenges of enforcement, governance, and supply chain security require a holistic platform.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",[746],"Omer Azaria","2026-02-27","Anthropic recently announced Claude Code Security, an AI system that detects vulnerabilities and proposes fixes. The market reacted immediately, with security stocks dipping as investors questioned whether AI might replace traditional AppSec tools. The question on everyone's mind: If AI can write code and secure it, is application security about to become obsolete?\n\nIf security only meant scanning code, the answer might be yes. But enterprise security has never been about detection alone.\n\nOrganizations are not asking whether AI can find vulnerabilities. They are asking three much harder questions: \n\n* Is what we are about to ship safe?  \n* Has our risk posture changed as environments evolve and dependencies, third-party services, tools, and infrastructure continuously shift?  \n* How do we govern a codebase that is increasingly assembled by AI and third-party sources, and that we are still accountable for? \n\nThose questions require a platform answer: Detection surfaces risk, but governance determines what happens next. \n\n[GitLab](https://about.gitlab.com/) is the orchestration layer built to govern the software lifecycle end-to-end. It gives teams the enforcement, visibility, and auditability they need to keep pace with the speed of AI-assisted development.\n\n## Trusting AI requires governing risk\n\nAI systems are rapidly getting better at identifying vulnerabilities and suggesting fixes. This is a meaningful and welcome advancement, but analysis is not accountability.\n\nAI cannot enforce company policy or define acceptable risk on its own. Humans must set the boundaries, policies, and guardrails that agents operate within, establishing separation of duties, ensuring audit trails, and maintaining consistent controls across thousands of repositories and teams. Trust in agents comes not from autonomy alone, but from clearly defined governance set by people. \n\nIn an [agentic world](https://about.gitlab.com/topics/agentic-ai/), where software is increasingly written and modified by autonomous systems, governance becomes more important, not less. The more autonomy organizations grant to AI, the stronger the governance must be.\n\nGovernance is not friction. It is the foundation that makes AI-assisted development trustworthy at scale.\n\n## LLMs see code, but platforms see context\n\nA large language model ([LLM](https://about.gitlab.com/blog/what-is-a-large-language-model-llm/)) evaluates code in isolation. An enterprise application security platform understands context. This difference matters because risk decisions are contextual:\n\n* Who authored the change?  \n* How critical is the application to the business?  \n* How does it interact with infrastructure and dependencies?  \n* Does the vulnerability exist in code that is actually reachable in production, or is it buried in a dependency that never executes?  \n* Is it actually exploitable in production, given how the application runs, its APIs, and the environment around it?\n\nSecurity decisions depend on this context. Without it, detection produces noisy alerts that slow down development rather than reducing risk. With it, organizations can triage quickly and manage risk effectively. Context evolves continuously as software changes, which means governance cannot be a one-time decision. \n\n## Static scans can’t keep up with dynamic risk\n\nSoftware risk is dynamic. Dependencies change, environments evolve, and systems interact in ways no single analysis can fully predict. A clean scan at one moment does not guarantee safety at release.\n\nEnterprise security depends on continuous assurance: controls embedded directly into development workflows that evaluate risk as software is built, tested, and deployed.\n\nDetection provides insight. Governance provides trust. Continuous governance is what allows organizations to ship safely at scale.\n\n## Governing the agentic future\n\nAI is reshaping how software is created. The question is no longer whether teams will use AI, but how safely they can scale it.\n\nSoftware today is assembled as much as it is written, from AI-generated code, open-source libraries, and third-party dependencies that span thousands of projects. Governing what ships across all of those sources is the hardest and most consequential part of application security, and it is the part that no developer-side tool is built to address. \n\nAs an intelligent orchestration platform, GitLab is built to address this problem. GitLab Ultimate embeds governance, policy enforcement, security scanning, and auditability directly into the workflows where software is planned, built, and shipped, so security teams can govern at the speed of AI. \n\nAI will accelerate development dramatically. The organizations that benefit most from AI will not be those with the smartest assistants alone, but those that build trust through strong governance.\n\n> To learn how GitLab helps organizations [govern and ship AI-generated code](https://about.gitlab.com/solutions/software-compliance/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_) safely, [talk to our team today](https://about.gitlab.com/sales/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_)\n\n\n ## Related reading\n\n - [Integrating AI with DevOps for enhanced security](https://about.gitlab.com/topics/devops/ai-enhanced-security/)\n - [The GitLab AI Security Framework for security leaders](https://about.gitlab.com/blog/the-gitlab-ai-security-framework-for-security-leaders/)\n - [Improve AI security in GitLab with composite identities](https://about.gitlab.com/blog/improve-ai-security-in-gitlab-with-composite-identities/)",[23,750],"security",{"featured":30,"template":13,"slug":752},"ai-can-detect-vulnerabilities-but-who-governs-risk",{"promotions":754},[755,768,779],{"id":756,"categories":757,"header":758,"text":759,"button":760,"image":765},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":761,"config":762},"Get your AI maturity score",{"href":763,"dataGaName":764,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":766},{"src":767},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":769,"categories":770,"header":771,"text":759,"button":772,"image":776},"devops-modernization",[24,568],"Are you just managing tools or shipping innovation?",{"text":773,"config":774},"Get your DevOps maturity score",{"href":775,"dataGaName":764,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":780,"categories":781,"header":782,"text":759,"button":783,"image":787},"security-modernization",[750],"Are you trading speed for security?",{"text":784,"config":785},"Get your security maturity score",{"href":786,"dataGaName":764,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":788},{"src":789},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":791,"blurb":792,"button":793,"secondaryButton":798},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":794,"config":795},"Get your free trial",{"href":796,"dataGaName":49,"dataGaLocation":797},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":799},{"href":53,"dataGaName":54,"dataGaLocation":797},1777310014978]