Skip to main content

Analytics Tool for MCP Servers - provides insights into MCP tool usage patterns

Project description

MCPcat Logo

Getting Started · Features · Docs · Website · Open Source · Discord

PyPI version PyPI downloads License: MIT Python GitHub issues CI

[!NOTE] Looking for the TypeScript SDK? Check it out here mcpcat-typescript.

MCPcat is an analytics platform for MCP server owners 🐱. It captures user intentions and behavior patterns to help you understand what AI users actually need from your tools — eliminating guesswork and accelerating product development all with one-line of code.

This SDK also provides a free and simple way to forward telemetry like logs, traces, and errors to any Open Telemetry collector or popular tools like Datadog and Sentry.

# Basic installation (includes official MCP SDK)
pip install mcpcat

# With Jlowin's/Prefect's FastMCP support
pip install "mcpcat[community]"

To learn more about us, check us out here

Why use MCPcat? 🤔

MCPcat helps developers and product owners build, improve, and monitor their MCP servers by capturing user analytics and tracing tool calls.

Use MCPcat for:

  • User session replay 🎬. Follow alongside your users to understand why they're using your MCP servers, what functionality you're missing, and what clients they're coming from.
  • Trace debugging 🔍. See where your users are getting stuck, track and find when LLMs get confused by your API, and debug sessions across all deployments of your MCP server.
  • Existing platform support 📊. Get logging and tracing out of the box for your existing observability platforms (OpenTelemetry, Datadog, Sentry) — eliminating the tedious work of implementing telemetry yourself.
mcpcat-diagram

Getting Started

To get started with MCPcat, first create an account and obtain your project ID by signing up at mcpcat.io. For detailed setup instructions visit our documentation.

Once you have your project ID, integrate MCPcat into your MCP server:

import mcpcat
from mcp.server import FastMCP

server = FastMCP(name="echo-mcp", version="1.0.0")

mcpcat.track(server, "proj_0000000")

Identifying users

You can identify your user sessions with a simple callback MCPcat exposes, called identify.

def identify_user(request, extra):
    user = myapi.get_user(request.params.arguments.token)
    return UserIdentity(
            user_id=user.id,
            user_name=user.name,
            user_data={
                "favorite_color": user.favorite_color,
            },
    )

mcpcat.track(server, "proj_0000000", MCPCatOptions(identify=identify_user))

Redacting sensitive data

MCPcat redacts all data sent to its servers and encrypts at rest, but for additional security, it offers a hook to do your own redaction on all text data returned back to our servers.

# Sync version
def redact_sync(text):
    return custom_redact(text)

mcpcat.track(server, "proj_0000000", redact_sensitive_information=redact_sync)

Forwarding data to existing observability platforms

MCPcat seamlessly integrates with your existing observability stack, providing automatic logging and tracing without the tedious setup typically required. Export telemetry data to multiple platforms simultaneously:

from mcpcat import MCPCatOptions, ExporterConfig

mcpcat.track(
    server, 
    "proj_0000000", # Or None if you just want to use the SDK to forward telemetry
    MCPCatOptions(
        exporters={
            # OpenTelemetry - works with Jaeger, Tempo, New Relic, etc.
            "otlp": ExporterConfig(
                type="otlp",
                endpoint="http://localhost:4318/v1/traces"
            ),
            # Datadog
            "datadog": ExporterConfig(
                type="datadog",
                api_key=os.getenv("DD_API_KEY"),
                site="datadoghq.com",
                service="my-mcp-server"
            ),
            # Sentry
            "sentry": ExporterConfig(
                type="sentry",
                dsn=os.getenv("SENTRY_DSN"),
                environment="production"
            )
        }
    )
)

Learn more about our free and open source telemetry integrations.

Free for open source

MCPcat is free for qualified open source projects. We believe in supporting the ecosystem that makes MCP possible. If you maintain an open source MCP server, you can access our full analytics platform at no cost.

How to apply: Email hi@mcpcat.io with your repository link

Already using MCPcat? We'll upgrade your account immediately.

Community Cats 🐱

Meet the cats behind MCPcat! Add your cat to our community by submitting a PR with your cat's photo in the docs/cats/ directory.

bibi zelda

Want to add your cat? Create a PR adding your cat's photo to docs/cats/ and update this section!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mcpcat-0.1.7rc1.tar.gz (3.3 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mcpcat-0.1.7rc1-py3-none-any.whl (51.2 kB view details)

Uploaded Python 3

File details

Details for the file mcpcat-0.1.7rc1.tar.gz.

File metadata

  • Download URL: mcpcat-0.1.7rc1.tar.gz
  • Upload date:
  • Size: 3.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.7.8

File hashes

Hashes for mcpcat-0.1.7rc1.tar.gz
Algorithm Hash digest
SHA256 cc7be7276ad2e52231ae6d42aceefec6d342a5b215cb1711dd066a867c149ee7
MD5 cfb2b3d72b2424401a13d9094a65d510
BLAKE2b-256 27e870040e5f3d9259891680735144703ac1482e97d891d42269fe686e372d72

See more details on using hashes here.

File details

Details for the file mcpcat-0.1.7rc1-py3-none-any.whl.

File metadata

  • Download URL: mcpcat-0.1.7rc1-py3-none-any.whl
  • Upload date:
  • Size: 51.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.7.8

File hashes

Hashes for mcpcat-0.1.7rc1-py3-none-any.whl
Algorithm Hash digest
SHA256 daa3ff87926fc697f290de234ae658923199714e83227d74acaddccdbe026ddc
MD5 712e8880d5ed45c1527b9ba4c5b56348
BLAKE2b-256 7e761de4403819460b98acaa6a195e2ca0f1bbcdf70849bc96b1c2f9945b3bf9

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page