How do I run MCP APP in development?
Use `uv sync`, then `source .venv/bin/activate`, followed by `mcp dev run`.
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Basic information
MCP Server Application with RAG
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-app": {
"command": "uv",
"args": [
"sync"
]
}
}
}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.
MCP APP is an MCP server application that provides RAG (Retrieval-Augmented Generation) and web searching tools for LLMs. It allows the LLM to retrieve knowledge from a vectorstore and add documents, augmenting the knowledge available to the model. It is designed for use with MCP clients such as Claude Desktop.
For development, run uv sync, then source .venv/bin/activate, followed by mcp dev run. To integrate with Claude Desktop, run mcp install server.py --env-file .env --with sqlalchemy --with pgvector --with openai --with "psycopg[binary]" --with pydantic --with python-dotenv --with tavily-python.
Use `uv sync`, then `source .venv/bin/activate`, followed by `mcp dev run`.
Run the command: `mcp install server.py --env-file .env --with sqlalchemy --with pgvector --with openai --with "psycopg[binary]" --with pydantic --with python-dotenv --with tavily-python`.
The README lists SQLAlchemy, pgvector, OpenAI, PostgreSQL, and additional packages: psycopg (binary), pydantic, python-dotenv, and tavily-python.
Yes, it uses PostgreSQL as the SQL database and PGVector as the vectorstore, with SQLAlchemy for ORM interaction.

Model Context Protocol Servers
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This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable,
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