gRPC Transport for MCP
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
gRPC Transport for MCP
Experimental / Proof of Concept
This package is a proof of concept for experimentation purposes. It is not intended for production use. The official Python package is being developed at GoogleCloudPlatform/mcp-grpc-transport-py — please follow/star that repo to stay updated.
A gRPC transport implementation for the Model Context Protocol (MCP) using FastMCP.
Background
SEP: gRPC as a native MCP transport — the specification enhancement proposal
Related MCP server: Agentforce MCP Integration Server
Features
Server: expose a FastMCP server over gRPC
Client: call a gRPC MCP server and get back native
mcp.typesobjectsTool calls with text and image content, structured output, and progress reporting
Proto definitions sourced from GoogleCloudPlatform/mcp-grpc-transport-proto
Installation
uv syncThe proto package (mcp-transport-proto) is fetched directly from GitHub via [tool.uv.sources] — no manual proto compilation needed.
Server
import asyncio
from mcp.server.fastmcp import FastMCP
from grpcmcp import serve_grpc
mcp = FastMCP("My Server")
@mcp.tool()
async def my_tool(x: int) -> str:
"""Does something useful."""
return str(x)
if __name__ == "__main__":
asyncio.run(serve_grpc(mcp)) # default: 0.0.0.0:50051
# asyncio.run(serve_grpc(mcp, port=9090))Client
import asyncio
from grpcmcp import GRPCClient
async def main():
async with GRPCClient("localhost", 50051) as client:
tools = await client.list_tools()
for tool in tools.tools:
print(f"{tool.name}: {tool.description}")
result = await client.call_tool("my_tool", {"x": 42})
for content in result.content:
if content.type == "text":
print(content.text)
asyncio.run(main())GRPCClient reference
Parameter | Type | Description |
|
| Server hostname |
|
| Server port |
|
| Default per-call timeout in seconds |
|
| Raw gRPC channel options |
Method | Returns |
|
|
|
|
Running the examples
Start the server:
uv run python example/grpc_example_server.pyIn another terminal, run the client:
uv run python example/grpc_example_client.pyRequirements
Python >= 3.10
grpcio >= 1.78.0
protobuf >= 4.25.0
mcp == 1.25.0
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
This server cannot be deployed
Maintenance
Related MCP Connectors
MCP server for progressive tool usage at any scale (see https://klavis.ai)
Chat, image, video, and audio models from many vendors behind one remote MCP server and one key.
MCP server for building and testing AI agents with multi-model experimentation and insights.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
Related MCP Servers
- AlicenseAqualityDmaintenanceMCP server for interacting with fal.ai models and services. Uses the latest streaming MCP support.82MIT
- AlicenseNot gradedqualityDmaintenanceEnables MCP clients to connect to LLM/API services using the Model Context Protocol, providing real-time interaction and tool access. Also offers RESTful API endpoints via FastAPI for programmatic integration with existing systems.Apache 2.0
- FlicenseNot gradedqualityDmaintenanceSets up a Model Context Protocol (MCP) server with FastMCP, allowing LLMs to use prompts, tools, and resources.-
- FlicenseNot gradedqualityDmaintenanceEnables building and running MCP servers over streamable HTTP, exposing tools to AI assistants like Cursor, with examples of mounting multiple servers in FastAPI.-