@Sreedeep-SS
Basic information
A Model Context Protocol (MCP) server
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"docret-mcp-server": {
"command": "python",
"args": [
"main.py"
]
}
}
}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.
Documentation Retrieval MCP Server (DOCRET) is a Model Context Protocol (MCP) server that enables AI assistants to access up-to-date documentation for Python libraries such as LangChain, LlamaIndex, and OpenAI. It dynamically fetches and provides relevant information from official documentation sources, ensuring AI applications always have access to the latest content.
Clone the repository, create and activate a virtual environment, install dependencies with pip install -r requirements.txt (or uv sync), set the SERPER_API_KEY environment variable in a .env file, and run python main.py. The server exposes a get_docs function that takes a query and library parameter. Integrate with AI assistants by adding a JSON configuration to the assistant’s MCP server list.
docs_urls dictionary.The Model Context Protocol is an open standard that enables secure, two-way connections between data sources and AI-powered tools. Developers can expose data through MCP servers or build AI applications (MCP clients) that connect to these servers.
You need Python 3.8 or higher, a package manager (UV or pip), a Serper API key, and Claude Desktop or Claude Code for testing.
The server currently supports LangChain, LlamaIndex, and OpenAI.
Add the library name and its documentation URL to the `docs_urls` dictionary in `main.py`. The server will then be able to search and retrieve documentation from that URL.
After setting up the environment and API key, run `python main.py` from the project root directory to launch the server.
Exact, shared memory for AI agents: a hierarchical database where totals always add up, every write is attributed to the agent and person behind it, and any figure traces back to its source file. Agents create databases,
Serve your docs to AI agents over SSH and MCP. Agents use ls, cat, grep, find, and pipes against a virtual filesystem backed by your documentation. Identity-scoped access, governed writing with human approval, and portab
Shared, serverless, conflict-free memory for AI agents: a multi-machine drop-in for the MCP memory knowledge graph.
Person-owned, portable AI memory served over MCP. Your knowledge, preferences, and decisions — captured in one assistant, available in all of them.
Sooveryn gives Claude Code a team of AI personas that remembers your project. Soov, the partner and team lead, keeps your decisions and how you work in a lasting memory, and finds them again by words and by meaning, from
Comments