DevRev MCP Server

What does “DevRev” MCP Server do?

The DevRev MCP Server is a Model Context Protocol (MCP) server designed to provide comprehensive access to DevRev’s APIs, enabling seamless integration of DevRev’s platform functionalities into AI assistants and developer workflows. Through this server, users can interact programmatically with DevRev to manage work items (such as issues and tickets), handle parts (enhancements), perform advanced searches across DevRev data, and retrieve user information. By exposing these capabilities, the DevRev MCP Server allows AI agents and clients to automate, query, and manage DevRev resources, supporting use cases like database queries, workflow automations, and context-aware development assistance.

List of Prompts

No prompt templates are explicitly mentioned in the provided repository files or documentation.

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List of Resources

No explicit MCP resources are listed in the available documentation or code. Resource primitives are not detailed in the README or visible files.

List of Tools

• search: Search for information across DevRev using the search API with support for different namespaces (articles, issues, tickets, parts, dev_users, accounts, rev_orgs).
• get_current_user: Fetch details about the currently authenticated DevRev user.
• get_work: Retrieve comprehensive information about a specific DevRev work item using its ID.
• create_work: Create new issues or tickets in DevRev with properties like title, body, assignees, and associated parts.
• update_work: Update existing work items by modifying properties such as title, body, assignees, or associated parts.
• list_works: List and filter work items based on criteria like state, dates, assignees, parts, and more.
• get_part: Get detailed information about a specific part (enhancement) using its ID.
• create_part: Create new parts with properties such as name, description, assignees, and parent parts.
• update_part: Update existing parts by modifying properties like name, description, assignees, or target dates.
• list_parts: List and filter parts based on criteria such as dates, assignees, parent parts, and more.

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Use Cases of this MCP Server

• Work Item Management: Developers can programmatically create, update, retrieve, and list issues or tickets, streamlining project management workflows and automation.
• Enhanced Part (Enhancement) Management: Teams can manage enhancements (called “parts”) by creating, updating, or organizing them hierarchically, supporting feature planning and tracking.
• Advanced Search: Perform hybrid and namespace-specific searches across articles, issues, users, and more, allowing AI assistants to surface relevant DevRev knowledge quickly.
• User Context Retrieval: Access information about the current user to enable personalized AI workflows, such as tailored notifications or context-aware suggestions.
• Automated Reporting and Analytics: By filtering and listing work items and parts with various criteria, teams can generate reports and insights for project tracking and decision-making.

How to set it up

Windsurf

No Windsurf-specific instructions are provided in the available documentation.

Claude

1. Obtain your DevRev API Key by signing up at https://app.devrev.ai/signup and following authentication instructions.
2. Locate your Claude Desktop configuration file:
   • MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%/Claude/claude_desktop_config.json
3. Edit the claude_desktop_config.json file to add the DevRev MCP server:

   "mcpServers": {
     "devrev": {
       "command": "uvx",
       "args": [
         "devrev-mcp"
       ],
       "env": {
         "DEVREV_API_KEY": "YOUR_DEVREV_API_KEY"
       }
     }
   }
   

4. Save the file and restart Claude Desktop.
5. Verify that the DevRev MCP server is accessible within the Claude interface.

Note: For development or unpublished servers, use the following configuration:

"mcpServers": {
  "devrev": {
    "command": "uv",
    "args": [
      "--directory",
      "Path to src/devrev_mcp directory",
      "run",
      "devrev-mcp"
    ],
    "env": {
      "DEVREV_API_KEY": "YOUR_DEVREV_API_KEY"
    }
  }
}

Cursor

No Cursor-specific instructions are provided in the available documentation.

Cline

No Cline-specific instructions are provided in the available documentation.

Securing API Keys

API keys are configured using the env section in your configuration JSON:

"env": {
  "DEVREV_API_KEY": "YOUR_DEVREV_API_KEY"
}

This keeps your API key secure and out of your codebase.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "devrev": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “devrev” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

| Roots Support | ⛔ | Not mentioned |

Our opinion:
Based on the available documentation, the DevRev MCP Server provides clear tool definitions and setup instructions for Claude, but lacks prompt templates, explicit resource definitions, and information on sampling or roots support. The project does have an open-source license, at least one tool, and some community activity, but would benefit from more comprehensive documentation and multi-platform instructions.

MCP Score

MCP Rating: 5/10
While the project is functional with good core tool coverage and open licensing, it is missing some key MCP features (prompts, resources, sampling, roots) and more robust cross-platform setup instructions.