Pydantic Logfire MCP Server

This repository contains a Model Context Protocol (MCP) server with tools that can access the OpenTelemetry traces and metrics you've sent to Pydantic Logfire.

This MCP server enables LLMs to retrieve your application's telemetry data, analyze distributed traces, and make use of the results of arbitrary SQL queries executed using the Pydantic Logfire APIs.

Available Tools

find_exceptions_in_file - Get detailed trace information about exceptions in a specific file
- Required arguments:
  - filepath (string): Path to the file to analyze
  - age (int): Number of minutes to look back (max 7 days)
arbitrary_query - Run custom SQL queries on your OpenTelemetry traces and metrics
- Required arguments:
  - query (string): SQL query to execute
  - age (int): Number of minutes to look back (max 7 days)
get_logfire_records_schema - Get the OpenTelemetry schema to help with custom queries
- No required arguments

Setup

Install `uv`

The first thing to do is make sure uv is installed, as uv is used to run the MCP server.

For installation instructions, see the uv installation docs.

If you already have an older version of uv installed, you might need to update it with uv self update.

Obtain a Pydantic Logfire read token

In order to make requests to the Pydantic Logfire APIs, the Pydantic Logfire MCP server requires a "read token".

You can create one under the "Read Tokens" section of your project settings in Pydantic Logfire: https://logfire.pydantic.dev/-/redirect/latest-project/settings/read-tokens

[!IMPORTANT] Pydantic Logfire read tokens are project-specific, so you need to create one for the specific project you want to expose to the Pydantic Logfire MCP server.

Manually run the server

Once you have uv installed and have a Pydantic Logfire read token, you can manually run the MCP server using uvx (which is provided by uv).

You can specify your read token using the LOGFIRE_READ_TOKEN environment variable:

LOGFIRE_READ_TOKEN=YOUR_READ_TOKEN uvx logfire-mcp@latest

or using the --read-token flag:

uvx logfire-mcp@latest --read-token=YOUR_READ_TOKEN

[!NOTE] If you are using Cursor, Claude Desktop, Cline, or other MCP clients that manage your MCP servers for you, you do NOT need to manually run the server yourself. The next section will show you how to configure these clients to make use of the Pydantic Logfire MCP server.

Configuration with well-known MCP clients

Configure for Cursor

Create a .cursor/mcp.json file in your project root:

{
  "mcpServers": {
    "logfire": {
      "command": "uvx",
      "args": ["logfire-mcp@latest", "--read-token=YOUR-TOKEN"]
    }
  }
}

The Cursor doesn't accept the env field, so you need to use the --read-token flag instead.

Configure for Claude Desktop

Add to your Claude settings:

{
  "command": ["uvx"],
  "args": ["logfire-mcp@latest"],
  "type": "stdio",
  "env": {
    "LOGFIRE_READ_TOKEN": "YOUR_TOKEN"
  }
}

Configure for Cline

Add to your Cline settings in cline_mcp_settings.json:

{
  "mcpServers": {
    "logfire": {
      "command": "uvx",
      "args": ["logfire-mcp@latest"],
      "env": {
        "LOGFIRE_READ_TOKEN": "YOUR_TOKEN"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Configure for VS Code

Make sure you enabled MCP support in VS Code.

Create a .vscode/mcp.json file in your project's root directory:

{
  "servers": {
    "logfire": {
      "type": "stdio",
      "command": "uvx", // or the absolute /path/to/uvx
      "args": ["logfire-mcp@latest"],
      "env": {
        "LOGFIRE_READ_TOKEN": "YOUR_TOKEN",
      }
    }
  }
}

Example Interactions

Get details about exceptions from traces in a specific file:

{
  "name": "find_exceptions_in_file",
  "arguments": {
    "filepath": "app/api.py",
    "age": 1440
  }
}

Response:

[
  {
    "created_at": "2024-03-20T10:30:00Z",
    "message": "Failed to process request",
    "exception_type": "ValueError",
    "exception_message": "Invalid input format",
    "function_name": "process_request",
    "line_number": "42",
    "attributes": {
      "service.name": "api-service",
      "code.filepath": "app/api.py"
    },
    "trace_id": "1234567890abcdef"
  }
]

Run a custom query on traces:

{
  "name": "arbitrary_query",
  "arguments": {
    "query": "SELECT trace_id, message, created_at, attributes->>'service.name' as service FROM records WHERE severity_text = 'ERROR' ORDER BY created_at DESC LIMIT 10",
    "age": 1440
  }
}

Examples of Questions for Claude

"What exceptions occurred in traces from the last hour across all services?"
"Show me the recent errors in the file 'app/api.py' with their trace context"
"How many errors were there in the last 24 hours per service?"
"What are the most common exception types in my traces, grouped by service name?"
"Get me the OpenTelemetry schema for traces and metrics"
"Find all errors from yesterday and show their trace contexts"

Getting Started

First, obtain a Pydantic Logfire read token from: https://logfire.pydantic.dev/-/redirect/latest-project/settings/read-tokens

Run the MCP server:

uvx logfire-mcp@latest --read-token=YOUR_TOKEN

Configure your preferred client (Cursor, Claude Desktop, or Cline) using the configuration examples above
Start using the MCP server to analyze your OpenTelemetry traces and metrics!

Contributing

We welcome contributions to help improve the Pydantic Logfire MCP server. Whether you want to add new trace analysis tools, enhance metrics querying functionality, or improve documentation, your input is valuable.

For examples of other MCP servers and implementation patterns, see the Model Context Protocol servers repository.

License

Pydantic Logfire MCP is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License.