What dependencies are required?
Node.js, npm, Python 3.x, and Ollama for local LLM support. The MCP server and mock service need npm packages; the client needs `langchain-ollama` and `mcp-use`.
@thappatan
Basic information
This is my Example of MCP Server for connect to books service, for my blog
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"MCP-Books-Service": {
"command": "python",
"args": [
"-m",
"venv",
".venv"
]
}
}
}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 Blog Project demonstrates the implementation of the Model Context Protocol (MCP) in a blog application system. It consists of three components—a TypeScript MCP server, an Express mock service for book data, and a Python client that integrates with a local language model via Ollama—that together enable structured data operations through natural language queries.
Start the mock service (cd books-mock-service && npm run dev), then in a separate terminal run the Python client (cd mcp-client && python client.py). Enter queries when prompted; type exit to quit.
@modelcontextprotocol/sdklangchain_ollama for LLM integrationNode.js, npm, Python 3.x, and Ollama for local LLM support. The MCP server and mock service need npm packages; the client needs `langchain-ollama` and `mcp-use`.
It uses the stdio transport layer defined by the Model Context Protocol. The server handles protocol messages between the Python client and the Express mock service.
Data is provided by the books-mock-service, a lightweight Express.js server with mock endpoints for book-related operations. It runs locally and can be extended.
Yes. Add new routes in `books-mock-service/server.js`. The MCP server (TypeScript) can be extended by editing files in `books-mcp-server/src` and rebuilding.
The client uses Ollama via `langchain_ollama`, allowing you to run a local language model. The LLM configuration can be customized in `mcp-client/client.py`.

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