https://github.com/mcpcat/mcpcat-python-sdk

MCPcat is an analytics platform for MCP server owners 🐱.
https://github.com/mcpcat/mcpcat-python-sdk

agent-runtime ai ai-agents ai-platform ai-tools analytics debugging mcp mcp-client mcp-server mcp-tools mcps observability

Last synced: 3 months ago
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MCPcat is an analytics platform for MCP server owners 🐱.

Host: GitHub
URL: https://github.com/mcpcat/mcpcat-python-sdk
Owner: MCPCat
License: mit
Created: 2025-05-31T00:56:13.000Z (11 months ago)
Default Branch: main
Last Pushed: 2025-06-17T18:19:59.000Z (10 months ago)
Last Synced: 2025-06-17T18:50:29.007Z (10 months ago)
Topics: agent-runtime, ai, ai-agents, ai-platform, ai-tools, analytics, debugging, mcp, mcp-client, mcp-server, mcp-tools, mcps, observability
Language: Python
Homepage: https://mcpcat.io
Size: 3.01 MB
Stars: 4
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-mcp-devtools - MCPcat - User analytics, session tracking, and live debugging for MCPs (SDKs / Python)

README

          


  

    

    

    

  





    Getting Started

     · 

    Features

     · 

    Docs

     · 

    Website

     · 

    Open Source

     · 

    Schedule a Demo





  

  

  

  

  

  



> [!NOTE]

> Looking for the TypeScript SDK? Check it out here [mcpcat-typescript](https://github.com/mcpcat/mcpcat-typescript-sdk).

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. 

```bash

# 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](https://mcpcat.io)

## 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.



## Getting Started

To get started with MCPcat, first create an account and obtain your project ID by signing up at [mcpcat.io](https://mcpcat.io). For detailed setup instructions visit our [documentation](https://docs.mcpcat.io).

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

```python

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`.

```python

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.

```python

# 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:

```python

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](https://docs.mcpcat.io/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.



  

  



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