Aider MCP Server

An MCP server based on Aider, providing AI programming capabilities and supporting multiple models and transport protocols.

Developer tools Artificial intelligence chatbots #AI programming #Code generation #Multi-model #MCP protocol .Python

rating : 2 points

downloads : 10

update time : 2025-04-28

What is Aider MCP Server?

Aider MCP Server is an intelligent programming assistant server that uses advanced AI models to help developers write, fix, and optimize code. Through standardized API interfaces, it can be integrated into various development environments to provide intelligent suggestions and automated support for programming tasks.

How to use Aider MCP Server?

You can interact with the server through simple API calls or command-line tools. The server supports two connection methods: SSE (Server-Sent Events) and standard input/output (stdio), which are suitable for integration requirements in different scenarios.

Applicable scenarios

Suitable for development scenarios that require AI-assisted programming, including code generation, error fixing, code optimization, and function addition. It is particularly suitable for rapid prototype development and the process of learning programming.

Main features

AI code generationAutomatically generate code based on natural language descriptions, supporting multiple programming languages

Model selectionSupports multiple AI models (GPT, Claude, Gemini, etc.), and you can select the most suitable model according to the task

Multi-transport protocol supportSupports two communication methods, SSE and stdio, for easy integration in different environments

Flexible configurationEasily configure the server behavior through environment variables and command-line parameters

Advantages and limitations

Advantages

Reduce development costs: Reduce repetitive coding work through AI assistance

Multi-model support: You can select the most suitable AI model for the current task

Easy to integrate: Provides standardized APIs for easy integration with existing toolchains

Flexible configuration: Supports multiple operation modes and parameter adjustments

Limitations

Requires API keys: Using some AI models requires corresponding service accounts

Network dependency: Requires a stable network connection to access AI services

Code quality depends on the model: The quality of the generated code is limited by the capabilities of the selected AI model

How to use

Installation preparation

Ensure that Python 3.10 or a higher version is installed on the system and prepare the necessary API keys

Install the server

Clone the repository and install dependencies using uv or pip

Configure the environment

Copy and edit the .env file to set your API keys and other parameters

Start the server

Select the startup method (SSE or stdio) that suits your needs

Integrate and use

Connect the server to your development environment according to the client integration instructions

Usage examples

Add new featuresAdd new features to an existing codebase

Fix code errorsFind and fix errors in the code

Code optimizationOptimize the performance or readability of existing code

Frequently Asked Questions

Which API keys do I need to use all the features?

What's the difference between SSE and stdio modes?

How to select the most suitable AI model?

How to ensure the quality of the generated code?

Related resources

Aider project homepage

The official repository of the Aider project

MCP protocol documentation

The official documentation of the Machine Cognition Protocol

Example configuration repository

A code repository containing various integration examples

🚀 Aider MCP Server

An MCP (Machine Cognition Protocol) server powered by Aider to provide AI programming capabilities.

🚀 Quick Start

The Aider MCP server offers programming capabilities through a standardized API by leveraging Aider, a powerful AI programming assistant. By offloading tasks to Aider, we can reduce costs while harnessing its features.

✨ Features

AI Code Generation: Automatically generate high - quality code.
Model Selection: Choose the appropriate model according to your needs.
Flexible Configuration: Support custom settings to meet the requirements of different scenarios.

📦 Installation

Prerequisites

Python Version: Python 3.10 or higher is required.
API Key: Configure the required API access key.

Installation Steps

# Example installation command
pip install aider-mcp-server

📚 Documentation

Configuration

Configure using environment variables:

Property	Details
`TRANSPORT`	Transport protocol, optional values are `sse` or `stdio`. Default is `sse`.
`EDITOR_MODEL`	The model used by the editor, default is `gemini/gemini-2.5-pro-preview-03-25`.
`GEMINI_API_KEY`	Gemini API access key.

Running the Server

Using the Command Line

# Example running command
python -m aider_mcp_server --port 8050

Using a Custom Configuration File

Create a configuration file config.json:

{
  "port": 8050,
  "models": {
    "default": "gemini/gemini-2.5-pro-preview-03-25"
  }
}

MCP Tools

AI Programming Tool

Features

Code Generation: Automatically generate code snippets.
Model Selection: Support multiple programming models.

Example Configuration

{
  "ai_coding_prompt": "Implement a function to calculate the factorial",
  "relative_editable_files": ["math.py"],
  "settings": {
    "auto_commits": false,
    "use_git": false
  }
}

Model Management Tool

Features

Model Listing: List all available models.
Filtering Function: Filter models by substrings.

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"
        ]
      }
    }
  }

Using Command - Line Tools

# Example running command
python -m aider_mcp_server --config config.json

Troubleshooting

Common issues and solutions:

Unable to connect to the server: Check if the server is running on the correct port.
Model loading failed: Ensure that the API key is correct and valid.