LangChain MCP Demo

This repository demonstrates a small Model Context Protocol style workflow in Python.

It shows how to:

• expose reusable tools through MCP

• keep a local document corpus in the repo

• retrieve grounded context from those documents

• generate a short OpenAI-backed answer from the retrieved context

• run the same project as a CLI for quick checks

What’s Inside

• MCP tools for listing, searching, and summarizing local documents

• a CLI mode for quick smoke testing

• a small local knowledge base in docs/

• a test suite for non-network logic

• project docs that cover architecture, configuration, testing, and security

Related MCP server: RAG MCP Server

Setup

cd C:\Projects\MCP
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
pip install -r requirements-dev.txt
Copy-Item .env.example .env

Set OPENAI_API_KEY in your environment or .env.

Run

python app.py

The demo starts a local MCP server on stdin/stdout for tool use by default and also provides a CLI mode for quick testing.

To run the HTTP transport instead:

python app.py --transport streamable-http

Architecture

flowchart LR
  User[User or Client] --> MCP[MCP Server]
  MCP --> Docs[(Local Markdown Docs)]
  MCP --> List[list_docs]
  MCP --> Search[search_docs]
  MCP --> Summary[summarize_docs]
  User --> CLI[CLI Question]
  CLI --> Retriever[Document Scoring]
  Retriever --> Context[Top Matching Context]
  Context --> LLM[OpenAI Chat Model]
  LLM --> Answer[Grounded Answer]

Request Flow

1. A client or CLI user asks a question.

2. The project loads the local documents from docs/.

3. The document scorer ranks the best matches.

4. The selected context is passed to OpenAI with a grounding instruction.

5. The response is returned with the retrieved source set.

Project Structure

• app.py: MCP server and CLI entry point

• docs/: architecture, configuration, security, and sample knowledge

• tests/: unit tests for non-network logic

• requirements.txt: runtime dependencies

• requirements-dev.txt: test dependencies

• .env.example: required environment variables

Configuration

Required:

• OPENAI_API_KEY

Optional:

• OPENAI_MODEL: defaults to gpt-4o-mini

• MCP_HOST: defaults to 127.0.0.1

• MCP_PORT: defaults to 8000

• MCP_TRANSPORT: defaults to stdio

Run Modes

CLI mode

python app.py --cli "What does this demo project do?"

MCP stdio mode

python app.py

MCP HTTP mode

python app.py --transport streamable-http

Transport Modes

• stdio: default and best for local agent connections

• streamable-http: useful when a client connects over HTTP

• sse: available for compatibility with older MCP clients

Testing

python -m pytest

The unit tests cover scoring and preview logic without network access. The CLI mode can be used for a live smoke test when OPENAI_API_KEY is set.

Security

Do not commit .env, logs, caches, or API keys. This project uses the same OPENAI_API_KEY environment variable as the earlier AI projects.

If a secret ever appears in a commit or pushed log, rotate it immediately and rewrite the affected history before treating the repo as clean.

Troubleshooting

• If OPENAI_API_KEY is missing, the CLI will stop before calling the API.

• If the MCP SDK is not installed, install dependencies from requirements.txt.

• If a client needs HTTP, use --transport streamable-http and the configured host and port.

• If the GitHub About box still looks empty, set the repository description, website, and topics in GitHub settings. README content does not populate that panel automatically.