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Aider MCP Server - Experimental

@disler

Repository

Basic information

Category

Other

Transports

stdio

Author

disler

About Aider MCP Server - Experimental

Minimal MCP Server for Aider

Aider MCP Server - Experimental config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "aider-mcp-server": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

Aider MCP Server - Experimental tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Aider MCP Server - Experimental?

A Model Context Protocol server that allows Claude Code to offload AI coding tasks to Aider, the open source AI coding assistant. It helps reduce costs, gives control over the coding model, and allows Claude Code to operate orchestratively by reviewing and revising code.

How to use Aider MCP Server - Experimental?

Clone the repository, run uv sync, configure API keys in .env, then add the server to Claud Code via claude mcp add with the appropriate --editor-model and --current-working-dir. Once running, use the aider_ai_code tool with a prompt and editable files, or list_models to discover available models.

Key features of Aider MCP Server - Experimental

  • Offloads AI coding tasks from Claude Code to Aider.
  • Uses the aider_ai_code tool with prompt and file paths.
  • Lists available models matching a substring via list_models.
  • Supports multiple AI model providers (OpenAI, Anthropic, Gemini, etc.).
  • Configurable editor model for separate coding and editing.
  • Operates over stdio transport with environment-based API keys.

Use cases of Aider MCP Server - Experimental

  • Refactoring a function in a Python file to handle exceptions.
  • Creating a new file based on natural language instructions.
  • Discovering which models are available for a given substring.
  • Using a cheaper or more capable model for editing than for initial coding.

Frequently asked questions

1

What dependencies are required?

Python, uv, and API keys for the chosen AI model (set in `.env` or the `env` section of `mcpServers`).

2

How do I specify which model Aider should use?

Use the `--editor-model` argument when adding the server, and optionally the `model` parameter in `aider_ai_code` calls.

3

Where are my project files located during execution?

All file paths are relative to the `--current-working-dir` specified in the server configuration.

4

What transport does the server use?

It uses `stdio` transport, as defined in the `.mcp.json` configuration.

5

Can I test the server locally?

Yes, tests are run with `uv run pytest` and require a valid Gemini API key for AI coding tests.

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