Aider
FreeNot checkedWebSocket server that exposes Aider's AI coding capabilities through the Model Context Protocol (MCP) for programmatic access
About
WebSocket server that exposes Aider's AI coding capabilities through the Model Context Protocol (MCP) for programmatic access
README
Quick Start
This is a real MCP (Model Context Protocol) server that lets you control Aider programmatically via WebSocket. It's like having Aider as an API.
# 1. Install
npm install
cp .env.example .env
# Add your OPENAI_API_KEY=sk-... to .env
# 2. Run the MCP server (recommended)
npm run mcp
# 3. Test it
python3 test-debug.py
What you get: Send natural language commands to Aider, get back created/edited files and full responses. Each client gets isolated workspaces. Works with any MCP-compatible client (Claude Desktop, custom apps, etc.).
Overview
A production-ready MCP (Model Context Protocol) wrapper that exposes Aider's functionality over WebSocket, enabling editor plugins and services to interact with Aider programmatically. This project provides two server implementations:
src/mcp-server.ts- Real MCP Protocol Server (Recommended)- Implements standard JSON-RPC 2.0 MCP protocol
- Proper MCP methods:
initialize,tools/list,tools/call - Smart completion detection (waits for Aider to finish)
- Compatible with Claude Desktop and other MCP clients
src/index.ts- Custom WebSocket Wrapper- Custom protocol with hello/welcome handshake
- Direct stdio pipe to Aider process
- Token-based authentication
Architecture
┌─────────────────┐ JSON-RPC 2.0 ┌─────────────────┐
│ MCP Client │ ◄─────────────────────────► │ MCP Server │
│ (Any MCP app) │ WebSocket │ (mcp-server.ts)│
└─────────────────┘ └────────┬────────┘
│
│ spawn()
▼
┌─────────────────┐
│ Aider Process │
│ (CLI instance) │
└─────────────────┘
Features
- Full MCP Protocol Support: Standard MCP methods and JSON-RPC 2.0
- Multi-tenant Isolation: One Aider child process per client
- Automatic Completion Detection: Waits for Aider to complete tasks
- Isolated Workspaces: Each session gets its own workspace directory
- OpenAI API Integration: Works with your existing OpenAI API key
- Real-time Communication: WebSocket-based bidirectional communication
- Flexible Configuration: Environment variables and CLI arguments
- Structured Logging: JSON logging with Pino
Installation
# Clone and install
git clone <repo-url>
cd aider_mcp
npm install
# Configure environment
cp .env.example .env
# Edit .env and add your OpenAI API key:
# OPENAI_API_KEY=sk-...
Prerequisites
- Node.js 18+
- Aider installed and available in PATH
- OpenAI API key
Usage
Running the Servers
# Run MCP protocol server (recommended)
npm run mcp
# Run custom WebSocket wrapper
npm run dev
# Production build
npm run build && npm start
Testing
# Quick test of MCP server
python3 test-debug.py
# Full test with waiting
python3 test-mcp-wait.py
# JavaScript test client
node test-mcp-client.js
MCP Protocol Communication
Available Tools
The MCP server exposes these tools to clients:
aider_command- Execute natural language commands{ "name": "aider_command", "arguments": { "command": "create a REST API with Flask" } }aider_add_file- Add files to Aider's context{ "name": "aider_add_file", "arguments": { "filename": "app.py" } }aider_run_command- Run shell commands through Aider{ "name": "aider_run_command", "arguments": { "command": "npm test" } }
Connection Flow
// 1. Connect via WebSocket
const ws = new WebSocket('ws://localhost:8080');
// 2. Initialize MCP session
ws.send(JSON.stringify({
"jsonrpc": "2.0",
"method": "initialize",
"params": {
"protocolVersion": "0.1.0",
"capabilities": {},
"clientInfo": {
"name": "your-client",
"version": "1.0.0"
}
},
"id": 1
}));
// 3. Call tools after initialization
ws.send(JSON.stringify({
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "aider_command",
"arguments": {
"command": "create a Python hello world script"
}
},
"id": 2
}));
Configuration
Environment Variables
# Required
OPENAI_API_KEY=sk-... # Your OpenAI API key
# Optional
PORT=8080 # WebSocket server port (MCP server default)
PORT=7011 # WebSocket server port (custom wrapper default)
AIDER_MODEL=gpt-4o-mini # Default model
WORKSPACES_DIR=./workspaces # Where to create project directories
LOG_LEVEL=info # Logging verbosity (debug|info|warn|error)
VALID_TOKENS=token1,token2 # Auth tokens for custom wrapper (empty = no auth)
CLI Arguments
Override configuration at runtime:
# Custom port and workspace
npm run mcp -- --port 9000
npm run dev -- --port 8080 --workspaces ./custom-workspaces
# Override environment variables (custom wrapper only)
npm run dev -- --set AIDER_MODEL=gpt-4 --set LOG_LEVEL=debug
Integration Examples
Claude Desktop App
Add to Claude Desktop config:
{
"mcpServers": {
"aider": {
"command": "node",
"args": ["/path/to/aider_mcp/dist/mcp-server.js"],
"env": {
"OPENAI_API_KEY": "your-key",
"AIDER_MODEL": "gpt-4o-mini"
}
}
}
}
Custom MCP Client
import { WebSocket } from 'ws';
class AiderMCPClient {
constructor(url = 'ws://localhost:8080') {
this.ws = new WebSocket(url);
this.requestId = 1;
this.setup();
}
async callTool(toolName, args) {
return this.sendRequest('tools/call', {
name: toolName,
arguments: args
});
}
sendRequest(method, params) {
const id = this.requestId++;
this.ws.send(JSON.stringify({
jsonrpc: '2.0',
method,
params,
id
}));
}
}
Project Structure
aider_mcp/
├── src/
│ ├── index.ts # Custom WebSocket wrapper
│ └── mcp-server.ts # Real MCP protocol server
├── workspaces/ # Auto-generated client workspaces
├── test-*.py/js # Various test clients
├── package.json # Node.js dependencies
├── tsconfig.json # TypeScript configuration
├── .env # Environment configuration
└── README.md # This file
Scripts
npm run mcp- Start MCP protocol server (recommended)npm run dev- Start custom WebSocket wrapper with hot reloadnpm run build- Compile TypeScript to JavaScriptnpm start- Start production servernpm run lint- Run ESLintnpm run typecheck- Run TypeScript type checking
Troubleshooting
Common Issues
"ConnectionRefusedError" or "Connect call failed"
- Server isn't running. Start it with
npm run mcp - Wrong port. Check
.envfile (MCP default: 8080, Custom default: 7011)
- Server isn't running. Start it with
"Aider not found"
# Install Aider pip install aider-chat # Verify installation which aider"No API key" or Aider errors
- Add your OpenAI API key to
.env:
OPENAI_API_KEY=sk-proj-...- Add your OpenAI API key to
Server returns too quickly / incomplete responses
- Fixed in MCP server - waits for Aider to complete (3-second silence detection)
"Tool execution timeout"
- Normal for complex tasks
- The MCP server waits as long as needed
Debug Mode
# Run with debug logging
LOG_LEVEL=debug npm run mcp
Checking Results
Files created by Aider are stored in workspace directories:
# List all workspaces
ls -la workspaces/
# Check latest workspace
ls -la workspaces/mcp-*/
# View created files
cat workspaces/mcp-*/your-file.py
Security Considerations
- Isolated Workspaces: Each client session gets its own directory
- Authentication: Custom wrapper supports token-based auth via
VALID_TOKENS - Git Disabled: Runs with
--no-gitby default for safety - API Key Security: Never commit
.envfile - Production: Consider running in Docker for additional isolation
Performance Notes
- First request to Aider may take longer (model loading)
- Subsequent requests are faster
- The server maintains one Aider process per session
- Workspace cleanup is manual (delete old directories as needed)
License
MIT
Installing Aider
This server has no published package — it is built from source. Open the repository and follow its README.
▸ github.com/larock22/aider-mcpFAQ
Is Aider MCP free?
Yes, Aider MCP is free — one-click install via Unyly at no cost.
Does Aider need an API key?
No, Aider runs without API keys or environment variables.
Is Aider hosted or self-hosted?
Self-hosted: the server runs locally on your machine via the install command above.
How do I install Aider in Claude Desktop, Claude Code or Cursor?
Open Aider on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.
Related MCPs
Fetch
Web content fetching and conversion for efficient LLM usage.
AWS KB Retrieval
Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.
by modelcontextprotocolSpring AI MCP Server
Provides auto-configuration for setting up an MCP server in Spring Boot applications.
llm-analysis-assistant
A very streamlined mcp client that supports calling and monitoring stdio/sse/streamableHttp, and can also view request responses through the /logs page. It also
by xuzexin-hzCompare Aider with
Not sure what to pick?
Find your stack in 60 seconds
Author?
Embed badge for your README
Browse similar
All ai MCPs
