devin-mcp
Enables Claude Code to delegate implementation tasks to Devin.
README
Devin MCP Server
An MCP server that enables Claude Code to delegate implementation tasks to Devin.
Installation
Option 1: Install from Local Path (Development)
If you have the devin-mcp repository cloned locally:
# Navigate to the devin-mcp directory
cd /path/to/devin-mcp
# Install with uv (creates venv automatically)
uv pip install -e .
Option 2: Install from Git Repository
# Install directly from git (if repository is public)
uv pip install git+https://github.com/iainmck29/devin-mcp.git
# Or from a specific branch/tag
uv pip install git+https://github.com/iainmck29/devin-mcp.git@main
Option 3: Install as a Published Package
# If published to PyPI (future)
uv pip install devin-mcp
Note: The MCP server needs to be installed in a location where uv can find it. If installing from a local path, use an absolute path or ensure the path is accessible.
Configuration
Set your Devin API key and playbook ID as environment variables:
export DEVIN_API_KEY="your-api-key-here"
export DEVIN_PLAYBOOK_ID="your-playbook-id-here"
Usage
Running the Server
# Via module (using uv run to automatically use project venv)
uv run python -m devin_mcp.server
# Or via CLI entry point
uv run devin-mcp
Claude Code Configuration
Step 1: Get Your Devin API Key
- Sign in to Devin
- Navigate to your account settings or API section
- Generate or copy your API key
Step 2: Locate Claude Code Settings File
The MCP settings file location depends on your OS:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json - Linux:
~/.config/Claude/claude_desktop_config.json
Step 3: Configure the MCP Server
Open the settings file and add the devin server configuration. If the file doesn't exist or is empty, create it with this structure:
{
"mcpServers": {
"devin": {
"command": "uv",
"args": ["run", "python", "-m", "devin_mcp.server"],
"env": {
"DEVIN_API_KEY": "your-api-key-here",
"DEVIN_PLAYBOOK_ID": "your-playbook-id-here"
}
}
}
}
Important Notes:
- Replace
"your-api-key-here"with your actual Devin API key - Replace
"your-playbook-id-here"with your Devin playbook ID - If you installed from a local path, ensure
uvis in your PATH - The
uvcommand must be able to find the installeddevin_mcppackage - If you installed in a specific virtual environment, you may need to use the full path to
uvor activate that environment
Step 4: Restart Claude Code
After saving the configuration file:
- Completely quit Claude Code (not just close the window)
- Reopen Claude Code
- The MCP server should automatically start and connect
Step 5: Verify It's Working
- Open Claude Code in any project
- Check the MCP status indicator (usually in the bottom status bar)
- Try asking Claude: "What MCP tools are available?" or "List the devin tools"
- You should see the four Devin tools:
devin_run_phase,devin_await_completion,devin_get_status,devin_send_message
Troubleshooting
Server won't start:
- Verify
uvis installed:which uvoruv --version - Check that
devin_mcpis installed:uv run python -c "import devin_mcp; print('OK')" - Check Claude Code logs for error messages
API key errors:
- Verify the API key is correct in the config file
- Ensure there are no extra quotes or whitespace around the key
- Test the API key manually:
export DEVIN_API_KEY="your-key" && uv run python -c "from devin_mcp.client import DevinClient; print('OK')"
Tools not appearing:
- Restart Claude Code completely
- Check the MCP connection status in Claude Code
- Verify the config JSON is valid (no trailing commas, proper quotes)
Available Tools
devin_run_phase
Create a new Devin session with a playbook and prompt.
Parameters:
prompt(string, required): The task promptplaybook_id(string, optional): The Devin playbook ID. UsesDEVIN_PLAYBOOK_IDenv var if not provided.
Returns: { success, session_id, url } or { success: false, error }
devin_await_completion
Poll a session until it reaches a terminal state.
Parameters:
session_id(string, required): The session to monitortimeout(integer, optional): Max wait in seconds (default: 600)
Returns: Full session details or raises DevinTimeoutError
devin_get_status
Quick, non-blocking status check.
Parameters:
session_id(string, required): The session to check
Returns: { success, session_id, url, status_enum } or { success: false, error }
devin_send_message
Send a follow-up message to a session.
Parameters:
session_id(string, required): The session to messagemessage(string, required): The message content
Returns: { success, detail } or { success: false, error }
Example Workflow
# 1. Start a phase (uses DEVIN_PLAYBOOK_ID from environment)
result = devin_run_phase(
prompt="thoughts/shared/plans/2024-01-15-auth.md, phase 1, branch feature/auth"
)
# Or override with a specific playbook:
# result = devin_run_phase(prompt="...", playbook_id="pb_abc123")
session_id = result["session_id"]
# 2. Wait for completion
session = devin_await_completion(session_id, timeout=600)
# 3. Check result
if session["status_enum"] == "finished":
print("Phase completed successfully!")
elif session["status_enum"] == "blocked":
# Send clarification if needed
devin_send_message(session_id, "Use the existing User model from src/models/user.py")
Development
# Install dev dependencies
uv pip install -e ".[dev]"
# Run tests (using uv run to ensure correct environment)
uv run pytest
# Type check
uv run mypy src/
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.