Which integrations are available for this server?

Provides tools for fetching the current Bitcoin price in various currencies via the CoinGecko API.

How do I use MCP Server + AI Agent?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Server + AI Agent What's the weather in London?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP Server + AI Agent

by amitsharma55

Overview Schema Related Servers Score Discussions

Python

Remote

MCP Server + AI Agent

A Python MCP server with JWT authentication and sample tools (weather + Bitcoin price), plus an AI agent that uses OpenAI to reason about and call those tools.

Architecture

User prompt → OpenAI GPT-4o (decides tool calls) → MCP Client → MCP Server (localhost:8000)
                    ↑                                    ↓
                    └──────── tool results ──────────────┘

MCP Server: mcp Python SDK (FastMCP) + Starlette, Streamable HTTP at POST /mcp
Auth: HS256 JWT — self-signed tokens, validated by the server
Tools: Auto-discovered via MCP protocol — add tools to the server, agent sees them automatically
Agent: Interactive CLI that connects to the MCP server and uses OpenAI for reasoning

Related MCP server: Weather Service MCP

Project Structure

mcp-server/
├── pyproject.toml
├── Dockerfile
├── .env.example
├── .env                        ← your secrets (git-ignored)
├── scripts/
│   └── generate_token.py       ← mint JWT tokens
├── src/                        ← MCP Server
│   ├── __init__.py
│   ├── config.py               ← loads env vars
│   ├── auth.py                 ← HS256 JWT verifier
│   ├── server.py               ← FastMCP + Starlette app
│   └── tools/
│       ├── __init__.py
│       ├── weather.py          ← Open-Meteo weather tool
│       └── crypto.py           ← CoinGecko Bitcoin price tool
└── agent/                      ← AI Agent
    ├── __init__.py
    ├── mcp_client.py           ← connects to MCP server
    ├── llm.py                  ← OpenAI integration
    └── main.py                 ← interactive CLI

Prerequisites

Tool	Install
Python 3.12+	`brew install python@3.12`
uv (package manager)	`brew install uv`
Docker (optional)	Docker Desktop for Mac

Setup

1. Install dependencies

cd /path/to/mcp-server
uv sync

This installs all Python packages (mcp, httpx, pyjwt, uvicorn, openai, etc.) into a local .venv.

2. Create your `.env` file

cp .env.example .env

3. Generate a JWT secret

openssl rand -hex 32

Paste the output as JWT_SECRET in .env.

4. Generate a JWT token

uv run python scripts/generate_token.py

This prints a JWT token valid for 30 days. Paste it as MCP_TOKEN in .env.

Options:

uv run python scripts/generate_token.py --sub amit --days 60

5. Add your API keys to `.env`

JWT_SECRET=<from step 3>
MCP_SERVER_URL=http://localhost:8000/mcp
MCP_TOKEN=<from step 4>
OPENAI_API_KEY=<your OpenAI API key>

MCP Server

Start the server

uv run uvicorn src.server:app --port 8000

The server runs at http://localhost:8000. Press Ctrl+C to stop.

Test the server

Health check

curl http://localhost:8000/health

Expected: {"status":"ok"}

Test unauthenticated access (should be blocked)

curl -s -w "\nHTTP %{http_code}\n" -X POST http://localhost:8000/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","method":"initialize","id":1,"params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'

Expected: 401 Unauthorized

Test authenticated access

TOKEN=$(uv run python scripts/generate_token.py)

# Initialize
curl -s -X POST http://localhost:8000/mcp \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","method":"initialize","id":1,"params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}' | python3 -m json.tool

Expected: JSON with protocolVersion, capabilities, and serverInfo.

List tools

curl -s -X POST http://localhost:8000/mcp \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","method":"tools/list","id":2}' | python3 -m json.tool

Expected: Two tools — get_weather and get_bitcoin_price with their input schemas.

Call the Bitcoin price tool

curl -s -X POST http://localhost:8000/mcp \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","method":"tools/call","id":3,"params":{"name":"get_bitcoin_price","arguments":{"currency":"usd"}}}' | python3 -m json.tool

Expected: Current Bitcoin price in USD.

Call the weather tool (needs API key)

curl -s -X POST http://localhost:8000/mcp \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","method":"tools/call","id":4,"params":{"name":"get_weather","arguments":{"city":"London"}}}' | python3 -m json.tool

The weather tool uses the free Open-Meteo API — no API key required.

AI Agent

The agent connects to the MCP server, auto-discovers tools, and uses OpenAI GPT-4o to decide when to call them.

Run the agent

Make sure the MCP server is running first (in a separate terminal), then:

uv run python -m agent.main

Expected output:

Connecting to MCP server...
Connected to: MCP Server v1.26.0
Available tools: get_weather, get_bitcoin_price

Type your question (or 'quit' to exit):

Example prompts

Prompt	What happens
`What's the Bitcoin price?`	Agent calls `get_bitcoin_price` → returns price
`What is Bitcoin trading at in EUR?`	Agent calls `get_bitcoin_price(currency="eur")`
`What's the weather in London?`	Agent calls `get_weather(city="London")` (needs API key)
`What's 2 + 2?`	Agent answers directly — no tool call
`quit`	Exits the agent

Tool calls are visible in real time:

You: What's the Bitcoin price?
  → Calling get_bitcoin_price({"currency": "usd"})
  ← Result received
Assistant: Bitcoin is at $70,487 USD, up 3.3% in the last 24h.

MCP Inspector (Debugging UI)

MCP Inspector is the official interactive debugging tool for MCP servers. It provides a web UI to connect, list tools, call them, and inspect raw JSON-RPC messages.

Launch the Inspector

No install needed — run it directly with npx:

npx @modelcontextprotocol/inspector

This opens a web UI at http://localhost:6274.

Connect to Your Server

Transport Type: Select Streamable HTTP
URL: http://localhost:8000/mcp
Authentication: Add a header:
- Header Name: Authorization
- Header Value: Bearer <your MCP_TOKEN from .env>

Important: The token value must start with Bearer (with a space) followed by your JWT token.

Click Connect

What You Can Do

List Tools — view all registered tools (get_weather, get_bitcoin_price, etc.)
Call Tools — fill in arguments and execute tool calls interactively
Inspect Messages — view raw JSON-RPC request/response traffic
Test Auth — verify your JWT tokens are working correctly

Stop the Inspector

Press Ctrl+C in the terminal where the inspector is running.

Docker (Optional)

Build and run

docker build -t mcp-server .
docker run -p 8000:8000 --env-file .env mcp-server

This builds a container image and runs the server on port 8000. The --env-file .env passes your secrets into the container.

Test

Same curl commands as above — the server is at http://localhost:8000.

Expose to Internet (Optional)

Cloudflare Tunnel (no account needed)

brew install cloudflared
cloudflared tunnel --url http://localhost:8000

This gives you a public https://*.trycloudflare.com URL. No signup required. Stop with Ctrl+C.

Cloud Run (Google Cloud)

gcloud run deploy mcp-server --source . --allow-unauthenticated

One command deploy. Tear down with:

gcloud run services delete mcp-server

Adding New Tools

Create a new file in src/tools/ (e.g. src/tools/my_tool.py)
Write an async function with type hints and a docstring:

async def my_tool(param: str) -> dict:
    """Description shown to the LLM."""
    return {"result": "..."}

from src.tools.my_tool import my_tool
mcp.tool()(my_tool)

Restart the server — the agent auto-discovers the new tool on next startup.

This server cannot be installed

license - not found

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

Need Help?

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MCP Server + AI Agent

Architecture

Project Structure

Prerequisites

Setup

1. Install dependencies

2. Create your .env file

3. Generate a JWT secret

4. Generate a JWT token

5. Add your API keys to .env

MCP Server

Start the server

Test the server

Health check

Test unauthenticated access (should be blocked)

Test authenticated access

List tools

Call the Bitcoin price tool

Call the weather tool (needs API key)

AI Agent

Run the agent

Example prompts

MCP Inspector (Debugging UI)

Launch the Inspector

Connect to Your Server

What You Can Do

Stop the Inspector

Docker (Optional)

Build and run

Test

Expose to Internet (Optional)

Cloudflare Tunnel (no account needed)

Cloud Run (Google Cloud)

Adding New Tools

Maintenance

Resources

Looking for Admin?

Latest Blog Posts

MCP directory API

2. Create your `.env` file

5. Add your API keys to `.env`