What is Aider Mcp Server

Aider-MCP-Server is an experimental Machine Cognition Protocol (MCP) server that utilizes Aider, an AI coding assistant, to provide coding capabilities through a standardized API.

Use cases

Use cases include generating code snippets, debugging existing code, enhancing code functionality, and providing coding suggestions in real-time.

How to use

To use Aider-MCP-Server, clone the repository, install the necessary packages, and configure the server settings. After setup, you can run coding tasks by interacting with the server via the API.

Key features

Key features include AI code generation for one-shot coding tasks, model selection for optimal task performance, flexible configuration options for Aider sessions, and multi-transport support for integration.

Where to use

Aider-MCP-Server can be used in software development environments, educational platforms for coding, and any application requiring automated coding assistance.

Aider MCP Server

A Machine Cognition Protocol (MCP) server that provides AI coding capabilities using Aider.

Table of Contents

• Overview
• Features
• Prerequisites
• Installation
• Configuration
• Running the Server
• MCP Tools
• Integration with MCP Clients
• Troubleshooting
• License

Overview

This MCP server leverages Aider, a powerful AI coding assistant, to provide coding capabilities via a standardized API. By discretely offloading work to Aider, we can reduce costs while using model-specific capabilities to create more reliable code through multiple focused LLM calls.

Features

• AI Code Generation: Run one-shot coding tasks with Aider to add, fix, or enhance code
• Model Selection: Query available models to choose the most appropriate one for your task
• Flexible Configuration: Configure Aider sessions with customizable settings
• Multi-transport Support: Run via Server-Sent Events (SSE) or stdio for flexible integration
• General Question Answering: Ask an LLM questions using a simple prompt

Prerequisites

• Python: 3.10 or higher
• Package Manager: uv (recommended) or pip
• API Keys: Depending on the models you want to use, you’ll need API keys for:
  • OpenAI (for GPT models)
  • Anthropic (for Claude models)
  • Google (for Gemini models)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/aider-mcp.git
   cd aider-mcp
   

2. Install the package:

   uv sync
   uv venv
   uv pip install -e .
   

3. Run the tests to ensure everything is working:

   uv run pytest
   

Configuration

Configure the server behavior using environment variables in a .env file:

# Create environment file from example
cp .env.example .env

Edit the .env file to configure transport, host, port, and API keys.

Variable	Description	Default	Required
TRANSPORT	Transport protocol (sse or stdio)	sse	No
HOST	Host to bind to when using SSE transport	0.0.0.0	No
PORT	Port to listen on when using SSE transport	8050	No
OPENAI_API_KEY	API key for OpenAI models		*
ANTHROPIC_API_KEY	API key for Anthropic models		*
GEMINI_API_KEY	API key for Google Gemini models		*

*Required only if using models from that provider

Command Line Options

• --editor-model: Model to use for editing (default: gemini/gemini-2.5-pro-exp-03-25)
• --architect-model: Model to use for architecture planning (optional)
• --cwd: Current working directory (default: current directory)

Running the Server

Start the Server (SSE Mode)

# Using the module directly
uv run python -m aider_mcp_server

Or with custom settings:

uv run python -m aider_mcp_server --editor-model "gemini/gemini-2.5-pro-exp-03-25" --cwd "/path/to/project"

You should see output similar to:

Starting server with transport: sse
Using SSE transport on 0.0.0.0:8050

Using stdio Mode

When using stdio mode, you don’t need to start the server separately - the MCP client will start it automatically when configured properly (see Integration with MCP Clients).

MCP Tools

The Aider MCP server exposes the following tools:

ai_code

Run Aider to perform coding tasks.

Parameters:

• ai_coding_prompt: The prompt for the AI coding task
• relative_editable_files: List of files that can be edited
• relative_readonly_files: (Optional) List of files that should be read-only
• settings: (Optional) Settings for the Aider session

Example:

{
  "ai_coding_prompt": "Add a function that calculates the factorial of a number",
  "relative_editable_files": [
    "math.py"
  ],
  "settings": {
    "auto_commits": false,
    "use_git": false
  }
}

get_models

List available Aider models filtered by substring.

Parameters:

• substring: Substring to filter models by

Example:

{
  "substring": "openai"
}

ask_question

Send a prompt to an LLM and return the response.

Parameters:

• prompt: The question or statement to send
• model: (Optional) The LLM model to use

Example:

{
  "prompt": "What is the capital of France?",
  "model": "gpt-4o"
}

Integration with MCP Clients

Configure your MCP client to connect to the SSE endpoint:

{
  "mcpServers": {
    "aider-mcp-server": {
      "transport": "sse",
      "serverUrl": "http://localhost:8050/sse"
    }
  }
}

Stdio Integration

Configure your MCP client to run the server via stdio:

{
  "mcpServers": {
    "aider-mcp-server": {
      "transport": "stdio",
      "command": "python",
      "args": [
        "-m",
        "aider_mcp_server"
      ],
      "env": {
        "TRANSPORT": "stdio"
      }
    }
  }
}

Integration with MCP Clients

Using Docker

{
  "mcpServers": {
    "aider-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--mount",
        "type=bind,source=<YOUR_FULL_PATH>",
        "-e",
        "TRANSPORT=stdio",
        "-e",
        "EDITOR_MODEL=gemini/gemini-2.5-pro-preview-03-25",
        "-e",
        "GEMINI_API_KEY=<YOUR_API_KEY>",
        "danielscholl/aider-mcp-server"
      ]
    }
  }
}