Glama
Neo4j MCP Server

# MCP Python SDK

    which python
    python3 -m venv myenv
    source myenv/Scripts/activate
    pip install -r requirements.txt
    python fast_mcp_server.py


# Usage
## to run the client
    
    add
        .env
        OPENAI_API_KEY=

## to run the server
    
    uvicorn ne04j_mcp_server:app --host 0.0.0.0 --port 8000

# Data Prep

## Insert Node:
    CREATE (p1:Person {name: "Tom Hanks", birthYear: 1956})
    CREATE (p2:Person {name: "Kevin Bacon", birthYear: 1958})
    CREATE (m1:Movie {title: "Forrest Gump", releaseYear: 1994})
    CREATE (m2:Movie {title: "Apollo 13", releaseYear: 1995})

## Insert Relationship
    MATCH (p:Person {name: "Tom Hanks"}), (m:Movie {title: "Forrest Gump"})
    CREATE (p)-[:ACTED_IN]->(m)

    MATCH (p:Person {name: "Tom Hanks"}), (m:Movie {title: "Apollo 13"})
    CREATE (p)-[:ACTED_IN]->(m)

    MATCH (p1:Person {name: "Tom Hanks"}), (p2:Person {name: "Kevin Bacon"})
    CREATE (p1)-[:FRIENDS_WITH]->(p2)

## Insert properties
    MATCH (p:Person {name: "Tom Hanks"})
    SET p.oscarsWon = 2

    MATCH (m:Movie {title: "Forrest Gump"})
    SET m.genre = "Drama"

    MATCH (p:Person {name: "Tom Hanks"})-[r:ACTED_IN]->(m:Movie {title: "Forrest Gump"})
    SET r.role = "Forrest Gump"

## Insert Complex Structure
    // Create a community and then match persons to add them to the community
    CREATE (c1:Community {name: "Hollywood Stars"})
    WITH c1
    MATCH (p:Person)
    WHERE p.name IN ["Tom Hanks", "Kevin Bacon"]
    CREATE (p)-[:MEMBER_OF]->(c1)
   
# other things - boiler plate - no clue - ignore for now.

<div align="center">

<strong>Python implementation of the Model Context Protocol (MCP)</strong>

[![PyPI][pypi-badge]][pypi-url]
[![MIT licensed][mit-badge]][mit-url]
[![Python Version][python-badge]][python-url]
[![Documentation][docs-badge]][docs-url]
[![Specification][spec-badge]][spec-url]
[![GitHub Discussions][discussions-badge]][discussions-url]

</div>

<!-- omit in toc -->
## Table of Contents

- [Overview](#overview)
- [Installation](#installation)
- [Quickstart](#quickstart)
- [What is MCP?](#what-is-mcp)
- [Core Concepts](#core-concepts)
  - [Server](#server)
  - [Resources](#resources)
  - [Tools](#tools)
  - [Prompts](#prompts)
  - [Images](#images)
  - [Context](#context)
- [Running Your Server](#running-your-server)
  - [Development Mode](#development-mode)
  - [Claude Desktop Integration](#claude-desktop-integration)
  - [Direct Execution](#direct-execution)
- [Examples](#examples)
  - [Echo Server](#echo-server)
  - [SQLite Explorer](#sqlite-explorer)
- [Advanced Usage](#advanced-usage)
  - [Low-Level Server](#low-level-server)
  - [Writing MCP Clients](#writing-mcp-clients)
  - [MCP Primitives](#mcp-primitives)
  - [Server Capabilities](#server-capabilities)
- [Documentation](#documentation)
- [Contributing](#contributing)
- [License](#license)

[pypi-badge]: https://img.shields.io/pypi/v/mcp.svg
[pypi-url]: https://pypi.org/project/mcp/
[mit-badge]: https://img.shields.io/pypi/l/mcp.svg
[mit-url]: https://github.com/modelcontextprotocol/python-sdk/blob/main/LICENSE
[python-badge]: https://img.shields.io/pypi/pyversions/mcp.svg
[python-url]: https://www.python.org/downloads/
[docs-badge]: https://img.shields.io/badge/docs-modelcontextprotocol.io-blue.svg
[docs-url]: https://modelcontextprotocol.io
[spec-badge]: https://img.shields.io/badge/spec-spec.modelcontextprotocol.io-blue.svg
[spec-url]: https://spec.modelcontextprotocol.io
[discussions-badge]: https://img.shields.io/github/discussions/modelcontextprotocol/python-sdk
[discussions-url]: https://github.com/modelcontextprotocol/python-sdk/discussions

## Overview

The Model Context Protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. This Python SDK implements the full MCP specification, making it easy to:

- Build MCP clients that can connect to any MCP server
- Create MCP servers that expose resources, prompts and tools
- Use standard transports like stdio and SSE
- Handle all MCP protocol messages and lifecycle events

## Installation

We recommend using [uv](https://docs.astral.sh/uv/) to manage your Python projects:

```bash
uv add "mcp[cli]"
```

Alternatively:
```bash
pip install mcp
```

## Quickstart

Let's create a simple MCP server that exposes a calculator tool and some data:

```python
# server.py
from mcp.server.fastmcp import FastMCP

# Create an MCP server
mcp = FastMCP("Demo")

# Add an addition tool
@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b

# Add a dynamic greeting resource
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
    """Get a personalized greeting"""
    return f"Hello, {name}!"
```

You can install this server in [Claude Desktop](https://claude.ai/download) and interact with it right away by running:
```bash
mcp install server.py
```

Alternatively, you can test it with the MCP Inspector:
```bash
mcp dev server.py
```

## What is MCP?

The [Model Context Protocol (MCP)](https://modelcontextprotocol.io) lets you build servers that expose data and functionality to LLM applications in a secure, standardized way. Think of it like a web API, but specifically designed for LLM interactions. MCP servers can:

- Expose data through **Resources** (think of these sort of like GET endpoints; they are used to load information into the LLM's context)
- Provide functionality through **Tools** (sort of like POST endpoints; they are used to execute code or otherwise produce a side effect)
- Define interaction patterns through **Prompts** (reusable templates for LLM interactions)
- And more!

## Core Concepts

### Server

The FastMCP server is your core interface to the MCP protocol. It handles connection management, protocol compliance, and message routing:

```python
# Add lifespan support for startup/shutdown with strong typing
from dataclasses import dataclass
from typing import AsyncIterator
from mcp.server.fastmcp import FastMCP

# Create a named server
mcp = FastMCP("My App")

# Specify dependencies for deployment and development
mcp = FastMCP("My App", dependencies=["pandas", "numpy"])

@dataclass
class AppContext:
    db: Database  # Replace with your actual DB type

@asynccontextmanager
async def app_lifespan(server: FastMCP) -> AsyncIterator[AppContext]:
    """Manage application lifecycle with type-safe context"""
    try:
        # Initialize on startup
        await db.connect()
        yield AppContext(db=db)
    finally:
        # Cleanup on shutdown
        await db.disconnect()

# Pass lifespan to server
mcp = FastMCP("My App", lifespan=app_lifespan)

# Access type-safe lifespan context in tools
@mcp.tool()
def query_db(ctx: Context) -> str:
    """Tool that uses initialized resources"""
    db = ctx.request_context.lifespan_context["db"]
    return db.query()
```

### Resources

Resources are how you expose data to LLMs. They're similar to GET endpoints in a REST API - they provide data but shouldn't perform significant computation or have side effects:

```python
@mcp.resource("config://app")
def get_config() -> str:
    """Static configuration data"""
    return "App configuration here"

@mcp.resource("users://{user_id}/profile")
def get_user_profile(user_id: str) -> str:
    """Dynamic user data"""
    return f"Profile data for user {user_id}"
```

### Tools

Tools let LLMs take actions through your server. Unlike resources, tools are expected to perform computation and have side effects:

```python
@mcp.tool()
def calculate_bmi(weight_kg: float, height_m: float) -> float:
    """Calculate BMI given weight in kg and height in meters"""
    return weight_kg / (height_m ** 2)

@mcp.tool()
async def fetch_weather(city: str) -> str:
    """Fetch current weather for a city"""
    async with httpx.AsyncClient() as client:
        response = await client.get(f"https://api.weather.com/{city}")
        return response.text
```

### Prompts

Prompts are reusable templates that help LLMs interact with your server effectively:

```python
@mcp.prompt()
def review_code(code: str) -> str:
    return f"Please review this code:\n\n{code}"

@mcp.prompt()
def debug_error(error: str) -> list[Message]:
    return [
        UserMessage("I'm seeing this error:"),
        UserMessage(error),
        AssistantMessage("I'll help debug that. What have you tried so far?")
    ]
```

### Images

FastMCP provides an `Image` class that automatically handles image data:

```python
from mcp.server.fastmcp import FastMCP, Image
from PIL import Image as PILImage

@mcp.tool()
def create_thumbnail(image_path: str) -> Image:
    """Create a thumbnail from an image"""
    img = PILImage.open(image_path)
    img.thumbnail((100, 100))
    return Image(data=img.tobytes(), format="png")
```

### Context

The Context object gives your tools and resources access to MCP capabilities:

```python
from mcp.server.fastmcp import FastMCP, Context

@mcp.tool()
async def long_task(files: list[str], ctx: Context) -> str:
    """Process multiple files with progress tracking"""
    for i, file in enumerate(files):
        ctx.info(f"Processing {file}")
        await ctx.report_progress(i, len(files))
        data, mime_type = await ctx.read_resource(f"file://{file}")
    return "Processing complete"
```

## Running Your Server

### Development Mode

The fastest way to test and debug your server is with the MCP Inspector:

```bash
mcp dev server.py

# Add dependencies
mcp dev server.py --with pandas --with numpy

# Mount local code
mcp dev server.py --with-editable .
```

### Claude Desktop Integration

Once your server is ready, install it in Claude Desktop:

```bash
mcp install server.py

# Custom name
mcp install server.py --name "My Analytics Server"

# Environment variables
mcp install server.py -v API_KEY=abc123 -v DB_URL=postgres://...
mcp install server.py -f .env
```

### Direct Execution

For advanced scenarios like custom deployments:

```python
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("My App")

if __name__ == "__main__":
    mcp.run()
```

Run it with:
```bash
python server.py
# or
mcp run server.py
```

## Examples

### Echo Server

A simple server demonstrating resources, tools, and prompts:

```python
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Echo")

@mcp.resource("echo://{message}")
def echo_resource(message: str) -> str:
    """Echo a message as a resource"""
    return f"Resource echo: {message}"

@mcp.tool()
def echo_tool(message: str) -> str:
    """Echo a message as a tool"""
    return f"Tool echo: {message}"

@mcp.prompt()
def echo_prompt(message: str) -> str:
    """Create an echo prompt"""
    return f"Please process this message: {message}"
```

### SQLite Explorer

A more complex example showing database integration:

```python
from mcp.server.fastmcp import FastMCP
import sqlite3

mcp = FastMCP("SQLite Explorer")

@mcp.resource("schema://main")
def get_schema() -> str:
    """Provide the database schema as a resource"""
    conn = sqlite3.connect("database.db")
    schema = conn.execute(
        "SELECT sql FROM sqlite_master WHERE type='table'"
    ).fetchall()
    return "\n".join(sql[0] for sql in schema if sql[0])

@mcp.tool()
def query_data(sql: str) -> str:
    """Execute SQL queries safely"""
    conn = sqlite3.connect("database.db")
    try:
        result = conn.execute(sql).fetchall()
        return "\n".join(str(row) for row in result)
    except Exception as e:
        return f"Error: {str(e)}"
```

## Advanced Usage

### Low-Level Server

For more control, you can use the low-level server implementation directly. This gives you full access to the protocol and allows you to customize every aspect of your server, including lifecycle management through the lifespan API:

```python
from contextlib import asynccontextmanager
from typing import AsyncIterator

@asynccontextmanager
async def server_lifespan(server: Server) -> AsyncIterator[dict]:
    """Manage server startup and shutdown lifecycle."""
    try:
        # Initialize resources on startup
        await db.connect()
        yield {"db": db}
    finally:
        # Clean up on shutdown
        await db.disconnect()

# Pass lifespan to server
server = Server("example-server", lifespan=server_lifespan)

# Access lifespan context in handlers
@server.call_tool()
async def query_db(name: str, arguments: dict) -> list:
    ctx = server.request_context
    db = ctx.lifespan_context["db"]
    return await db.query(arguments["query"])
```

The lifespan API provides:
- A way to initialize resources when the server starts and clean them up when it stops
- Access to initialized resources through the request context in handlers
- Type-safe context passing between lifespan and request handlers

```python
from mcp.server.lowlevel import Server, NotificationOptions
from mcp.server.models import InitializationOptions
import mcp.server.stdio
import mcp.types as types

# Create a server instance
server = Server("example-server")

@server.list_prompts()
async def handle_list_prompts() -> list[types.Prompt]:
    return [
        types.Prompt(
            name="example-prompt",
            description="An example prompt template",
            arguments=[
                types.PromptArgument(
                    name="arg1",
                    description="Example argument",
                    required=True
                )
            ]
        )
    ]

@server.get_prompt()
async def handle_get_prompt(
    name: str,
    arguments: dict[str, str] | None
) -> types.GetPromptResult:
    if name != "example-prompt":
        raise ValueError(f"Unknown prompt: {name}")

    return types.GetPromptResult(
        description="Example prompt",
        messages=[
            types.PromptMessage(
                role="user",
                content=types.TextContent(
                    type="text",
                    text="Example prompt text"
                )
            )
        ]
    )

async def run():
    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
        await server.run(
            read_stream,
            write_stream,
            InitializationOptions(
                server_name="example",
                server_version="0.1.0",
                capabilities=server.get_capabilities(
                    notification_options=NotificationOptions(),
                    experimental_capabilities={},
                )
            )
        )

if __name__ == "__main__":
    import asyncio
    asyncio.run(run())
```

### Writing MCP Clients

The SDK provides a high-level client interface for connecting to MCP servers:

```python
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

# Create server parameters for stdio connection
server_params = StdioServerParameters(
    command="python", # Executable
    args=["example_server.py"], # Optional command line arguments
    env=None # Optional environment variables
)

# Optional: create a sampling callback
async def handle_sampling_message(message: types.CreateMessageRequestParams) -> types.CreateMessageResult:
    return types.CreateMessageResult(
        role="assistant",
        content=types.TextContent(
            type="text",
            text="Hello, world! from model",
        ),
        model="gpt-3.5-turbo",
        stopReason="endTurn",
    )

async def run():
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write, sampling_callback=handle_sampling_message) as session:
            # Initialize the connection
            await session.initialize()

            # List available prompts
            prompts = await session.list_prompts()

            # Get a prompt
            prompt = await session.get_prompt("example-prompt", arguments={"arg1": "value"})

            # List available resources
            resources = await session.list_resources()

            # List available tools
            tools = await session.list_tools()

            # Read a resource
            content, mime_type = await session.read_resource("file://some/path")

            # Call a tool
            result = await session.call_tool("tool-name", arguments={"arg1": "value"})

if __name__ == "__main__":
    import asyncio
    asyncio.run(run())
```

### MCP Primitives

The MCP protocol defines three core primitives that servers can implement:

| Primitive | Control               | Description                                         | Example Use                  |
|-----------|-----------------------|-----------------------------------------------------|------------------------------|
| Prompts   | User-controlled       | Interactive templates invoked by user choice        | Slash commands, menu options |
| Resources | Application-controlled| Contextual data managed by the client application   | File contents, API responses |
| Tools     | Model-controlled      | Functions exposed to the LLM to take actions        | API calls, data updates      |

### Server Capabilities

MCP servers declare capabilities during initialization:

| Capability  | Feature Flag                 | Description                        |
|-------------|------------------------------|------------------------------------|
| `prompts`   | `listChanged`                | Prompt template management         |
| `resources` | `subscribe`<br/>`listChanged`| Resource exposure and updates      |
| `tools`     | `listChanged`                | Tool discovery and execution       |
| `logging`   | -                            | Server logging configuration       |
| `completion`| -                            | Argument completion suggestions    |

## Documentation

- [Model Context Protocol documentation](https://modelcontextprotocol.io)
- [Model Context Protocol specification](https://spec.modelcontextprotocol.io)
- [Officially supported servers](https://github.com/modelcontextprotocol/servers)

## Contributing

We are passionate about supporting contributors of all levels of experience and would love to see you get involved in the project. See the [contributing guide](CONTRIBUTING.md) to get started.

## License

This project is licensed under the MIT License - see the LICENSE file for details.