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MCP Server Playground

by deivit24
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Python
Remote

MCP Server Playground

A learning project for building and testing custom MCP (Model Context Protocol) servers connected to a deployed LiteLLM proxy.

What This Is

This project explores how to:

  • Build a REST API and automatically expose it as an MCP server

  • Register that MCP server with a LiteLLM proxy

  • Chat with an LLM that can call your custom tools via MCP

  • Test the whole flow from a CLI

Related MCP server: FastAPI SSE MCP Random

Architecture

┌─────────────┐     MCP protocol      ┌──────────────┐     HTTP      ┌──────────────┐
│  LiteLLM    │ ──────────────────── ▶│  MCP Server  │ ────────────▶ │  FastAPI     │
│  Proxy      │                       │  :8001       │               │  :8000       │
│  (deployed) │                       └──────────────┘               └──────────────┘
└─────────────┘
       ▲
       │  OpenAI-compatible API
       │
┌─────────────┐
│   CLI       │
│  (cli.py)   │
└─────────────┘

FastAPI (api/) — serves two endpoints:

  • GET /home — returns a random David Salazar fact

  • GET /faq — returns Japan FAQ Q&A pairs

MCP Server (mcp_server/) — wraps the FastAPI via its OpenAPI spec and exposes the routes as MCP tools. LiteLLM registers this server and makes the tools available to the LLM.

LiteLLM Proxy — deployed externally. Holds your Anthropic API key, registers your MCP server, and handles all LLM routing.

CLI (cli.py) — interactive chat tool. Lists available MCP tools, lets you pick one, then chats with the LLM using that tool.

Project Structure

faq/
├── api/
│   └── main.py          # FastAPI routes
├── mcp_server/
│   └── server.py        # MCP server (auto-generated from OpenAPI spec)
├── core/
│   ├── llm.py           # LLMService — async chat via LiteLLM proxy
│   ├── models.py        # Shared Pydantic models
│   └── settings.py      # Config loaded from .env
├── cli.py               # Interactive CLI chat tool
├── .env.example         # Environment variable template
└── pyproject.toml

Setup

1. Install dependencies

uv sync

2. Configure environment

cp .env.example .env

Edit .env with your values:

BASE_URL="http://localhost:8000/"
BASE_URL_DOCS="http://localhost:8000/openapi.json"
OPENAI_KEY="sk-your-litellm-master-key"
LITELLM_URL="https://your-litellm-proxy.com"   # or http://localhost:4000 locally

Running Locally

Start each service in a separate terminal:

# 1. FastAPI
uv run uvicorn api.main:app --reload --port 8000

# 2. MCP Server (requires FastAPI to be running first)
uv run python mcp_server/server.py

# 3. CLI chat tool (requires MCP server to be running)
uv run python cli.py

Deploying

Both api/ and mcp_server/ live in this repo and can be deployed together.

FastAPI

uvicorn api.main:app --host 0.0.0.0 --port 8000

MCP Server — update BASE_URL in your deployed .env to point to the deployed FastAPI URL, then:

python mcp_server/server.py

Register with LiteLLM — in your LiteLLM config.yaml, add:

mcp_servers:
  - name: faq
    url: https://your-mcp-server.com/mcp

Or

You can use the LiteLLM UI.

CLI Usage

uv run python cli.py
Available tools:
  1. faq-home_home_get — Returns a random fact about Me.
  2. faq-faq_faq_get  — Returns a list of frequently asked questions about Japan.

Pick a tool (number): 2

Using tool: faq-faq_faq_get  |  Type 'quit' to exit.

You: Do I need a visa to go to Japan?
Assistant: Based on the information available, **visa requirements for Japan depend on your nationality**:

**Citizens of about 68 countries** can enter Japan **visa-free for short stays**, typically up to **90 days**.

However, this varies by country, so I recommend checking the **Japanese Ministry of Foreign Affairs website** to confirm the specific requirements for your country of citizenship.

If your country isn't on the visa-free list, you'll need to apply for an appropriate visa before traveling to Japan.

You: quit

Adding New Tools

  1. Add a new route to api/main.py

  2. Restart the MCP server — it auto-discovers routes from the OpenAPI spec or the @mcp.tool decorator

  3. The new tool appears automatically in LiteLLM and the CLI

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

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