MCP-llms-txt is an MCP server designed for Awesome-llms-txt. It allows users to add documentation directly into conversations via MCP resources. The project includes features like resource fetching and integrates with tools such as Smithery and Claude Desktop.
MCP-llms-txt enhances conversational workflows by enabling seamless integration of documentation through MCP resources. It is particularly useful for users leveraging the Awesome-llms-txt framework, offering automation and extensibility via tools like Smithery and manual configuration options.
Developers, technical writers, and teams working with large language models (LLMs) and conversational AI systems can benefit from MCP-llms-txt. Specifically, those using the Awesome-llms-txt framework or integrating with Claude Desktop will find it valuable.
MCP-llms-txt can be installed either automatically via Smithery or manually by configuring the Claude Desktop client. The installation process involves setting up a config file and ensuring the required dependencies are met.
MCP-llms-txt should be used when integrating documentation into conversational workflows, especially in projects involving Awesome-llms-txt. It is also suitable for testing and development environments where MCP servers are utilized.
To install MCP-llms-txt via Smithery, run the following command: `npx -y @smithery/cli install @SecretiveShell/MCP-llms-txt --client claude`.
Yes, you can test the MCP server locally using the mcp-cli tool. Run the command: `npx -y "@wong2/mcp-cli" -c config.json`. The provided config file is pre-configured and does not require changes.
MCP-llms-txt is licensed under the MIT License, making it open-source and free to use, modify, and distribute.
Contributions are welcome! You can open an issue or submit a pull request on the project's repository to suggest improvements or report bugs.
The project includes essential files such as `.gitignore`, `Dockerfile`, `LICENSE`, `README.md`, `config.json`, `pyproject.toml`, `smithery.yaml`, and `uv.lock`. These files support setup, configuration, and deployment.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
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An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
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