by secretiveshell
Llms.txt boosts technical support by integrating documentation snippets for better code explanations and educational con
Retrieves structured documentation from websites' llms.txt files and converts Git repositories into text summaries for AI consumption.
Llms.txt is a community-built MCP server published by secretiveshell that provides AI assistants with tools and capabilities via the Model Context Protocol. Llms.txt boosts technical support by integrating documentation snippets for better code explanations and educational con It is categorized under productivity.
You can install Llms.txt in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Llms.txt is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Llms.txt has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Llms.txt is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Llms.txt is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Llms.txt is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated Llms.txt against two servers with overlapping tools; this profile had the clearer scope statement.
We evaluated Llms.txt against two servers with overlapping tools; this profile had the clearer scope statement.
I recommend Llms.txt for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Strong directory entry: Llms.txt surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We wired Llms.txt into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Llms.txt is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
showing 1-10 of 63
MCP server for Awesome-llms-txt. Add documentation directly into your conversation via mcp resources.
<a href="https://glama.ai/mcp/servers/kqwhhpe8l7"><img width="380" height="200" src="https://glama.ai/mcp/servers/kqwhhpe8l7/badge" alt="MCP-llms-txt MCP server" /></a>
View a setup guide + example usage on pulsemcp.com
To install MCP Server for Awesome-llms-txt for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @SecretiveShell/MCP-llms-txt --client claude
Setup your claude config like this:
{
"mcpServers": {
"mcp-llms-txt": {
"command": "uvx",
"args": ["mcp-llms-txt"],
"env": {
"PYTHONUTF8": "1"
}
}
}
}
Use mcp-cli to test the server:
npx -y "@wong2/mcp-cli" -c config.json
The config file is already setup and does not need to be changed since there are no api keys or secrets.
Contributions are welcome! Please open an issue or submit a pull request.
This project is licensed under the MIT License.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.