Enables LLMs to safely execute make targets for development, testing, and project management tasks.
MCP Server Make is a Model Context Protocol (MCP) server that provides make functionality, enabling large language models such as Claude to perform essential development tasks in a safe and controlled manner. This server integrates with AI applications through the Model Context Protocol, allowing complex build processes to be executed directly by the AI. By offering capabilities like running specific make targets, building projects, handling errors, and respecting working directory context, it significantly enhances the AI's ability to assist developers.
MCP Server Make leverages Model Context Protocol (MCP) to deliver its core features:
These capabilities make MCP Server Make a versatile tool for developers looking to streamline their development processes and leverage the power of large language models like Claude for tasks such as running tests, formatting code, or handling dependency management.
MCP Server Make is designed to work seamlessly with various AI applications through clear and standardized communication via Model Context Protocol. The protocol flow diagram below illustrates how data is structured and transmitted between the MCP client (AI application) and the MCP server:
graph TD;
A[AI Application] -->|MCP Client| B[MCP Server]
B --> C[Data Source/Tool]
style A fill:#e1f5fe
style B fill:#f3e5f5
style C fill:#e8f5e8
MCP Server Make currently supports the following MCP clients:
MCP Client | Resources | Tools | Prompts | Status |
---|---|---|---|---|
Claude Desktop | ✅ | ✅ | ✅ | Full Support |
Continue | ✅ | ✅ | ✅ | Full Support |
Cursor | ❌ | ✅ | ❌ | Tools Only |
This matrix helps users understand which MCP clients are fully supported and can benefit the most from the enhanced capabilities provided by MCP Server Make.
Getting started with MCP Server Make is straightforward. You can install it using either uv
or pip, ensuring that you have the necessary environment set up to run make commands safely.
uv
uv pip install mcp-server-make
Alternatively, use pip for a wider compatibility:
pip install mcp-server-make
Once installed, you can start the server with simple command-line options. These allow you to specify the Makefile and working directory if needed.
To run MCP Server Make using uvx
:
# Run with default Makefile in current directory
uvx mcp-server-make
# Run with specific Makefile and working directory
uvx mcp-server-make --make-path /path/to/Makefile --working-dir /path/to/working/dir
For more advanced configurations, you can integrate MCP Server Make into your existing workflows using the provided CLI options.
make test
).make lint
).make format
).make x
.make z
.To integrate MCP Server Make into applications like Claude Desktop, you need to configure the MCP client accordingly:
{
"mcpServers": {
"make": {
"command": "uvx",
"args": [
"mcp-server-make",
"--make-path", "/absolute/path/to/Makefile",
"--working-dir", "/absolute/path/to/working/dir"
]
}
}
}
MCP Server Make is designed to work well with a variety of systems and has the following performance metrics:
The server switches to the specified working directory if provided; otherwise, it uses the directory containing the Makefile. After executing make commands, it restores the original working directory, ensuring no unintended changes occur.
MCP Server Make handles a variety of common errors:
All errors are communicated through the Model Context Protocol, ensuring consistent feedback across different clients.
--make-path
flag points to a valid file path.env
can be used for specific needs.Contributions are welcome! If you wish to contribute, follow these steps:
make check
.MCP Server Make is part of a broader ecosystem that supports various AI applications. Explore additional resources like documentation, community forums, and issue trackers to stay updated on the latest developments in the MCP client landscape.
By implementing this documentation, developers can effectively leverage MCP Server Make to enhance their development workflows and ensure seamless integration with advanced AI applications through Model Context Protocol.
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