MCP Server Demo

.dockerignore

@@ -0,0 +1,7 @@
+__pycache__/
+*.pyc
+*.log
+.venv/
+logs/
+*.egg-info/
+*.dist-info/

Dockerfile

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+FROM python:3.10-slim
+
+# Create non-root user
+RUN useradd -m -u 1000 user
+
+# Environment
+ENV PATH="/home/user/.local/bin:$PATH"
+WORKDIR /app
+RUN chown -R user:1000 /app
+# Install pip and uv
+RUN pip install --upgrade pip && pip install uv
+
+# Copy project files
+COPY --chown=user:1000 . /app
+
+# Switch to non-root user
+USER user
+
+# Install dependencies
+RUN uv sync
+
+# Run the server
+CMD ["uv", "run", "/app/fastmcp_server.py", "--host", "0.0.0.0", "--port", "7860"]
+
+# Build the docker
+# docker build -t fastmcp-server .
+# run as
+# docker run -it --rm -p 7860:7860 fastmcp-server

README.md

@@ -1,12 +1,85 @@
----
-title: Mcpserver Demo
-emoji: 💻
-colorFrom: red
-colorTo: pink
-sdk: docker
-pinned: false
-license: apache-2.0
-short_description: test_mcp
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Simple MCP Server and Client 
+
+There are two ways to implement
+
+1: The easy way with FastMCP - Recommended
+
+All the samples in the [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk) are based on FastMCP.
+
+
+2: Directly with the SDK and an ASGI provider like [Starlette](https://www.starlette.io/) and then with uvicron
+
+Needless to say the former is easy, the latter is difficult has more boilerplate code and very verbose.
+
+## Package Manager used
+
+Note 'uv' is the package manager used
+Note `uv sync` and `source .venv/bin/activate` to run in the virtual environment with all dependecies
+
+or run with `uv run`
+
+## MCP Server and Client via  FastMCP
+
+
+### Running the Server
+```
+ python mcp/fastmcp_server.py 
+```
+
+### Running the  Client 
+
+```
+python mcp/fastmcp_client.py 
+```
+
+**Output** - which is the real meat; this can be fed to the LLM and LLM will generate python code that be parsed by client and invoked as a tool call
+
+```
+Available tools: [Tool(name='add', description='Add two numbers', inputSchema={'properties': {'a': {'title': 'A', 'type': 'integer'}, 'b': {'title': 'B', 'type': 'integer'}}, 'required': ['a', 'b'], 'type': 'object'}, annotations=None)]
+```
+And for the call, the tool result  output
+
+```
+Tool result: [TextContent(type='text', text='3', annotations=None)]
+```
+
+## To Run in Docker
+
+1. Build the Docker container
+
+```
+docker build -t fastmcp-server .
+````
+
+2. Run the Docker container
+
+```
+docker run -it --rm -p 7860:7860 fastmcp-server
+```
+
+3. Run the client
+
+```
+uv run fastmcp_client.py 
+```
+
+
+
+
+
+### MCP Server and Client directly (via ASGI container -Starlette) - Not Recommended
+
+Running the Server
+
+```
+python mcp/mcp_server_starlette.py
+```
+
+Running the client Client
+
+```
+ python mcp/mcp_client.py 
+```
+
+The FastMCP client can also be used here. The output is the same as above

fastmcp_client.py

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+"""
+Author: Alex Punnen
+Status:  Demo
+"""
+import asyncio
+from fastmcp import Client
+
+async def example():
+    async with Client("http://127.0.0.1:7860/mcp") as client:
+        await client.ping()
+        # List available tools
+        tools = await client.list_tools()
+        print("Available tools:", tools)
+        tool_result = await client.call_tool("add", {"a": "1", "b": "2"})
+        print("Tool result:", tool_result)
+
+
+
+if __name__ == "__main__":
+    asyncio.run(example())

fastmcp_clinet_llm.py

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+"""
+  Lets see how to use the MCP client with an LLM. So what does the MCP client do?
+  1. Lists the available tools
+  2. Gives the description of the tools and how to call them via the MCP protocol which is basically JSON-RPC
+
+Author: Alex Punnen
+Status:  Demo
+
+"""
+import asyncio
+from fastmcp import Client
+
+async def example():
+    async with Client("http://127.0.0.1:4200/mcp") as client:
+        await client.ping()
+        # List available tools
+        tools = await client.list_tools()
+        print("Available tools:", tools)
+        tool_result = await client.call_tool("add", {"a": "1", "b": "2"})
+        
+        print("Tool result:", tool_result)
+
+
+
+if __name__ == "__main__":
+    asyncio.run(example())

fastmcp_server.py

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+"""
+  This is much simple and based on FastMCP
+  
+  from https://github.com/modelcontextprotocol/python-sdk/?tab=readme-ov-file#quickstart
+  and from   https://gofastmcp.com/deployment/running-server
+  
+Author: Alex Punnen
+Status:  Demo
+
+"""
+from fastmcp import FastMCP
+
+mcp = FastMCP()
+
+# Assume that this is the tool you want to expose
+# Give all the types and description
+@mcp.tool()
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+if __name__ == "__main__":
+    # take host and port from command line
+    import argparse
+    parser = argparse.ArgumentParser(description="Run FastMCP server")
+    parser.add_argument("--host", type=str, default="0.0.0.0", help="Host address (default: 0.0.0.0)")
+    parser.add_argument("--port", type=int, default=7860, help="Port number (default: 7860)")
+
+    args = parser.parse_args()
+    mcp.run(
+        transport="streamable-http", # https://github.com/modelcontextprotocol/python-sdk/?tab=readme-ov-file#streamable-http-transport
+        host=args.host,
+        port=args.port,
+        path="/mcp",
+        log_level="debug",
+    )

mcp_client.py

@@ -0,0 +1,31 @@
+"""
+ Source :https://github.com/modelcontextprotocol/python-sdk/blob/6353dd192c41b891ef3bf1dfc093db46f6e2175a/README.md?plain=1#L779
+ 
+Author: Alex Punnen
+Status:  Demo
+"""
+
+from mcp.client.streamable_http import streamablehttp_client
+from mcp import ClientSession
+
+
+async def main():
+    # Connect to a streamable HTTP server
+    async with streamablehttp_client("http://127.0.0.0:4200/mcp") as (
+        read_stream,
+        write_stream,
+        _,
+    ):
+        # Create a session using the client streams
+        async with ClientSession(read_stream, write_stream) as session:
+            # Initialize the connection
+            await session.initialize()
+            # Call a tool
+            tools = await session.list_tools()
+            print("Available tools:", tools)
+            tool_result = await session.call_tool("add", {"a": 1, "b": 2})
+            print("Tool result:", tool_result)
+            
+if __name__ == "__main__":
+    import asyncio
+    asyncio.run(main())

mcp_server_starlette.py

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+"""
+A low-level MCP Server exposing prompts over Streamable HTTP, without FastMCP or any frameowkrs
+Note that SSE, Server-Sent Events, is depreciated by MCP now - it keeps the seesion alive and cost more in cloud deployment
+Streamable HTTP is the new way to go
+
+Example source :https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/servers/simple-streamablehttp-stateless/mcp_simple_streamablehttp_stateless/server.py
+
+Author: Alex Punnen
+Status:  Demo
+"""
+
+from mcp.server.lowlevel import Server
+from mcp.server.streamable_http_manager import StreamableHTTPSessionManager
+from starlette.applications import Starlette
+import uvicorn
+from starlette.routing import Mount
+from starlette.types import Receive, Scope, Send
+from collections.abc import AsyncIterator
+import contextlib
+import mcp.types as types
+
+def main(port: int = 4200) -> int:
+    
+    #---------------------------------------------------------------------------------------
+    #   Define the MCP Application and add the artifacts to expose
+    #   to the MCP Client . In this case it is just the prompts
+    #   see - https://github.com/modelcontextprotocol/python-sdk/?tab=readme-ov-file#prompts
+    #---------------------------------------------------------------------------------------
+
+    app = Server("mcp-server")
+
+    # Add an  tool
+    @app.call_tool()
+    async def add(name: str, arguments: dict) -> int:
+        # see https://github.com/modelcontextprotocol/python-sdk/blob/6353dd192c41b891ef3bf1dfc093db46f6e2175a/examples/servers/simple-tool/mcp_simple_tool/server.py#L31
+        """Add two numbers"""
+        print(f"Server received: a={name} (type={type(name)}), arguments={arguments} (type={type(arguments)})") # 
+        if name != "add":
+            raise ValueError(f"Unknown tool: {name}")
+        result =  int(arguments["a"]) + int(arguments["b"])
+        return [types.TextContent(type='text', text=str(result))]
+
+    @app.list_tools()
+    async def list_tools() -> list[types.Tool]:
+        return [
+            types.Tool(
+                name="add",
+                description=(
+                    "add two numbers and return the result. "
+                    
+                ),
+                inputSchema={
+                    "type": "object",
+                    "required": ["a", "b"],
+                    "properties": {
+                        "a": {
+                            "type": "integer",
+                            "description": "The first number to add",
+                        },
+                        "b": {
+                            "type": "integer",
+                            "description": "The second number to add",
+                        },
+                    },
+                },
+            )
+        ]
+#---------------------------------------------------------------------------------------
+#   ALL Boilerplate code below
+#---------------------------------------------------------------------------------------
+
+   # Create the session manager with true stateless mode
+    session_manager = StreamableHTTPSessionManager(
+        app=app,
+        event_store=None,
+        json_response=True,
+        stateless=True,
+    )
+
+    async def handle_streamable_http(
+        scope: Scope, receive: Receive, send: Send
+    ) -> None:
+        await session_manager.handle_request(scope, receive, send)
+
+    @contextlib.asynccontextmanager
+    async def lifespan(app: Starlette) -> AsyncIterator[None]:
+        """Context manager for session manager."""
+        async with session_manager.run():
+            print("Application started with StreamableHTTP session manager!")
+            try:
+                yield
+            
+            finally:
+                print("Application shutting down...")
+
+    # Create an ASGI application using the transport
+    starlette_app = Starlette(
+        debug=True,
+        routes=[
+            Mount("/mcp", app=handle_streamable_http),
+        ],
+        lifespan=lifespan,
+    )
+
+    # 3) Launch via Uvicorn
+    uvicorn.run(starlette_app, host="0.0.0.0", port=port)
+
+    return 0
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -0,0 +1,10 @@
+[project]
+name = "mcpserver-demo"
+version = "0.1.0"
+description = "A MCP Server that exposes a simple tool, through Streamable HTTP"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "fastmcp>=2.3.4",
+    "mcp[cli]>=1.9.0",
+]

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