Python

Prerequisites

Python with the asyncio backend under anyio - the default for anyio.run() and for both FastMCP flavors’ own runners. trio is not currently supported.
An MCPFY API key - see Get your API key if you don’t have one yet

There are three ways to use mcpfy-pulse. None of them edit your files for you - pick the one that matches your situation.

FastMCP
Raw mcp SDK
Someone else's server (no source)

Works for both mcp.server.fastmcp.FastMCP (bundled in the official mcp package) and the standalone fastmcp package - same call either way, since both expose the same underlying server object.

Install the package

pip install mcpfy-pulse

Set your API key

In your .env file:

MCPFY_API_KEY=mk_live_xxx

Instrument your app

Right before you call .run():

import os
from mcp.server.fastmcp import FastMCP  # or: from fastmcp import FastMCP
from mcpfy_pulse import instrument_fastmcp, TelemetryOptions

mcp = FastMCP("my-server")

instrument_fastmcp(mcp, TelemetryOptions(api_key=os.environ.get("MCPFY_API_KEY")))

mcp.run()  # telemetry now flows for stdio, SSE, and streamable-HTTP alike

instrument_fastmcp monkey-patches the one call every FastMCP transport funnels through internally (self._mcp_server.run(...)), so it doesn’t matter which transport you pick. If api_key is unset, it’s a complete no-op - mcp is returned untouched, so it’s safe to leave this in place across environments.

For anyone who wrote their own server directly on mcp.server.Server (or anything else that hands a Server.run() call a pair of read/write streams) and has the source in front of them.

Install the package

pip install mcpfy-pulse

Set your API key

In your .env file:

MCPFY_API_KEY=mk_live_xxx

Wrap your streams

Right before you run them. This works for any transport - stdio, SSE, StreamableHTTP - not just the stdio example below:

import os
from mcp.server.stdio import stdio_server
from mcpfy_pulse import with_mcpfy_telemetry, TelemetryOptions

async def main():
    async with stdio_server() as (read_stream, write_stream):
        read_stream, write_stream = with_mcpfy_telemetry(
            read_stream, write_stream,
            TelemetryOptions(api_key=os.environ.get("MCPFY_API_KEY")),
        )
        await server.run(read_stream, write_stream, server.create_initialization_options())

with_mcpfy_telemetry wraps the read/write stream pair - the two points every JSON-RPC message passes through regardless of which transport is underneath. If api_key is unset, it returns the original streams unchanged, so it’s safe to leave this in place across environments.

No install step needed if your client can run uvx - it fetches mcpfy-proxy automatically the first time it runs. Edit your MCP client’s config (claude_desktop_config.json, Cursor’s mcp.json, etc.) to route the command through the proxy:

// before:
"github": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"] }

// after:
"github": {
  "command": "uvx",
  "args": ["--from", "mcpfy-pulse", "mcpfy-proxy", "--", "npx", "-y", "@modelcontextprotocol/server-github"],
  "env": { "MCPFY_API_KEY": "mk_live_xxx" }
}

Prefer not to rely on uvx? Run pip install mcpfy-pulse yourself and use the plain mcpfy-proxy command instead.

mcpfy-proxy becomes the process your client spawns. It spawns the real command as its own child, sits in that child’s stdin/stdout, and forwards every byte unchanged while classifying JSON-RPC messages on the side. Works for any language - Python, Node, Go, Rust, anything - since it only ever reads newline-delimited JSON off a pipe.

Advanced options

TelemetryOptions fields all fall back to an environment variable if omitted:

Option	Env var	Default
`api_key`	`MCPFY_API_KEY`	(required to send data)
`endpoint`	`MCPFY_TELEMETRY_ENDPOINT`	`https://api.mcpfy.ai/v1/telemetry/ingest`
`flush_interval_ms`	-	`5000`
`max_batch_size`	-	`500`

What’s next

Once your server sends its first event, head to the dashboard to see it.

View telemetry in the dashboard

See tool-level usage, health scores, and quality signals once data starts flowing.

Getting started

MCPfy Cloud

MCPfy SDK

Pulse SDK

Prerequisites

Advanced options

What’s next

View telemetry in the dashboard

​Prerequisites

​Advanced options

​What’s next

View telemetry in the dashboard

Prerequisites

Advanced options

What’s next