How do I configure the backend connection?

Set environment variables BACKEND_TYPE (e.g., 'jaeger') and BACKEND_URL to point to your backend endpoint. For some backends like Traceloop, BACKEND_API_KEY is also required.

Is installation required to run the server?

No installation is required; you can run it directly via pipx or uvx to always use the latest version.

Can this server be used with Claude Desktop and Codeium?

Yes, it can be configured in Claude Desktop, Claude Code CLI, Codeium (Windsurf), and Cursor clients.

Does it provide specialized support for LLM observability?

Yes, it supports OpenLLMetry semantic conventions to analyze LLM traces, token usage, and model performance.

Opentelemetry MCP Server

opentelemetrytracingllm-observabilitymcp-serverfintech

MCP server connecting AI assistants to OpenTelemetry backends for trace and LLM observability queries.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "traceloop-opentelemetry-mcp-server": {
      "command": "uvx",
      "args": [
        "uvx"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

This MCP server enables AI agents to query and analyze traces from OpenTelemetry-compatible backends such as Jaeger, Tempo, Traceloop, and others. It provides specialized tools for LLM observability using OpenLLMetry semantic conventions, allowing inspection of trace errors, token usage, model performance, and slow or expensive operations. Use it to integrate trace data and LLM metrics into AI-assisted debugging and monitoring workflows.

Use cases

Search traces with advanced filters

Retrieve complete trace details

Find traces containing errors

Analyze LLM token usage and model performance

List available services and LLM models

Key features

Supports multiple OpenTelemetry backends (Jaeger, Tempo, Traceloop, etc.)

Search traces and spans with filtering

Aggregate LLM token usage metrics

Discover and analyze LLM models in use

Find slow or expensive LLM-related traces

Capabilities & Tool Schemas (10)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Opentelemetry MCP Server.

search_traces

Search traces with advanced filters

search_spans

Search individual spans

get_trace

Get complete trace details

get_llm_usage

Aggregate token usage metrics

list_services

List available services

find_errors

Find traces with errors

Documentation Overview

OpenTelemetry MCP Server

Query and analyze LLM traces with AI assistance. Ask Claude to find expensive API calls, debug errors, compare model performance, or track token usage—all from your IDE.

An MCP (Model Context Protocol) server that connects AI assistants to OpenTelemetry trace backends (Jaeger, Tempo, Traceloop), with specialized support for LLM observability through OpenLLMetry semantic conventions.

See it in action:

https://github.com/user-attachments/assets/e2106ef9-0a58-4ba0-8b2b-e114c0b8b4b9

Quick Start
Installation
Features
Configuration
Tools Reference
Example Queries
Common Workflows
Troubleshooting
Development
Support

Quick Start

No installation required! Configure your client to run the server directly from PyPI:

json

// Add to claude_desktop_config.json:
{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "pipx",
      "args": ["run", "opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Or use uvx (alternative):

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "uvx",
      "args": ["opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

That's it! Ask Claude: "Show me traces with errors from the last hour"

Installation

For End Users (Recommended)

bash

# Run without installing (recommended)
pipx run opentelemetry-mcp --backend jaeger --url http://localhost:16686

# Or with uvx
uvx opentelemetry-mcp --backend jaeger --url http://localhost:16686

This approach:

✅ Always uses the latest version
✅ No global installation needed
✅ Isolated environment automatically
✅ Works on all platforms

Per Client Integration

Claude Desktop

Configure the MCP server in your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Using pipx (recommended):

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "pipx",
      "args": ["run", "opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Using uvx (alternative):

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "uvx",
      "args": ["opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

For Traceloop backend:

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "pipx",
      "args": ["run", "opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "traceloop",
        "BACKEND_URL": "https://api.traceloop.com",
        "BACKEND_API_KEY": "your_traceloop_api_key_here"
      }
    }
  }
}

Using the repository instead of pipx?

If you're developing locally with the cloned repository, use one of these configurations:

Option 1: Wrapper script (easy backend switching)

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "/absolute/path/to/opentelemetry-mcp-server/start_locally.sh"
    }
  }
}

Option 2: UV directly (for multiple backends)

config.json

{
  "mcpServers": {
    "opentelemetry-mcp-jaeger": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/opentelemetry-mcp-server",
        "run",
        "opentelemetry-mcp"
      ],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Claude Code

Claude Code works with MCP servers configured in your Claude Desktop config. Once configured above, you can use the server with Claude Code CLI:

bash

# Verify the server is available
claude-code mcp list

# Use Claude Code with access to your OpenTelemetry traces
claude-code "Show me traces with errors from the last hour"

Codeium (Windsurf)

Open Windsurf
Navigate to Settings → MCP Servers
Click Add New MCP Server
Add this configuration:

Using pipx (recommended):

config.json

{
  "opentelemetry-mcp": {
    "command": "pipx",
    "args": ["run", "opentelemetry-mcp"],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Using uvx (alternative):

config.json

{
  "opentelemetry-mcp": {
    "command": "uvx",
    "args": ["opentelemetry-mcp"],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Using the repository instead?

config.json

{
  "opentelemetry-mcp": {
    "command": "uv",
    "args": [
      "--directory",
      "/absolute/path/to/opentelemetry-mcp-server",
      "run",
      "opentelemetry-mcp"
    ],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Cursor

Open Cursor
Navigate to Settings → MCP
Click Add new MCP Server
Add this configuration:

Using pipx (recommended):

config.json

{
  "opentelemetry-mcp": {
    "command": "pipx",
    "args": ["run", "opentelemetry-mcp"],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Using uvx (alternative):

config.json

{
  "opentelemetry-mcp": {
    "command": "uvx",
    "args": ["opentelemetry-mcp"],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Using the repository instead of pipx?

config.json

{
  "opentelemetry-mcp": {
    "command": "uv",
    "args": [
      "--directory",
      "/absolute/path/to/opentelemetry-mcp-server",
      "run",
      "opentelemetry-mcp"
    ],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Gemini CLI

Configure the MCP server in your Gemini CLI config file (~/.gemini/config.json):

Using pipx (recommended):

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "pipx",
      "args": ["run", "opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Using uvx (alternative):

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "uvx",
      "args": ["opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Then use Gemini CLI with your traces:

bash

gemini "Analyze token usage for gpt-4 requests today"

Using the repository instead?

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/opentelemetry-mcp-server",
        "run",
        "opentelemetry-mcp"
      ],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Prerequisites:

Python 3.11 or higher
pipx or uv installed

Optional: Install globally

If you prefer to install the command globally:

bash

# Install with pipx
pipx install opentelemetry-mcp

# Verify
opentelemetry-mcp --help

# Upgrade
pipx upgrade opentelemetry-mcp

Or with pip:

Terminal

pip install opentelemetry-mcp

Features

Core Capabilities

🔌 Multiple Backend Support - Connect to Jaeger, Grafana Tempo, or Traceloop
🤖 LLM-First Design - Specialized tools for analyzing AI application traces
🔍 Advanced Filtering - Generic filter system with powerful operators
📊 Token Analytics - Track and aggregate LLM token usage across models and services
⚡ Fast & Type-Safe - Built with async Python and Pydantic validation

Tools

Read the full README →View source on GitHub →

Related MCP Servers

View all in Finance & Fintech View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

199

Stargazers on the source repository.

Last commit

2mo ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Opentelemetry MCP Server

The server supports Jaeger, Tempo, Traceloop, and other OpenTelemetry-compatible backends.

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Technical Specs & Signals

CategoryFinance & Fintech

PricingFree

9/10 checks healthy over the last 33d

59Quality signal: Good · 59/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools27/30

Adoption & activity7/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 27d ago via OSV.dev · uvx (PyPI)

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MCP & Agent Skills for Automated Documentation, and codebase conventions + context

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More in 💰 Finance & Fintech →Best MCP servers for Finance & Fintech →Alternatives to Opentelemetry MCP Server →Install in Claude Desktop Install in Cursor Install in VS Code

Opentelemetry MCP Server

opentelemetrytracingllm-observabilitymcp-serverfintech

MCP server connecting AI assistants to OpenTelemetry backends for trace and LLM observability queries.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "traceloop-opentelemetry-mcp-server": {
      "command": "uvx",
      "args": [
        "uvx"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Overview

Use cases

Search traces with advanced filters

Retrieve complete trace details

Find traces containing errors

Analyze LLM token usage and model performance

List available services and LLM models

Key features

Supports multiple OpenTelemetry backends (Jaeger, Tempo, Traceloop, etc.)

Search traces and spans with filtering

Aggregate LLM token usage metrics

Discover and analyze LLM models in use

Find slow or expensive LLM-related traces

Capabilities & Tool Schemas (10)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Opentelemetry MCP Server.

search_traces

Search traces with advanced filters

search_spans

Search individual spans

get_trace

Get complete trace details

get_llm_usage

Aggregate token usage metrics

list_services

List available services

find_errors

Find traces with errors

Documentation Overview

OpenTelemetry MCP Server

Query and analyze LLM traces with AI assistance. Ask Claude to find expensive API calls, debug errors, compare model performance, or track token usage—all from your IDE.

See it in action:

https://github.com/user-attachments/assets/e2106ef9-0a58-4ba0-8b2b-e114c0b8b4b9

Quick Start
Installation
Features
Configuration
Tools Reference
Example Queries
Common Workflows
Troubleshooting
Development
Support

Quick Start

No installation required! Configure your client to run the server directly from PyPI:

json

// Add to claude_desktop_config.json:
{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "pipx",
      "args": ["run", "opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Or use uvx (alternative):

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "uvx",
      "args": ["opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

That's it! Ask Claude: "Show me traces with errors from the last hour"

Installation

For End Users (Recommended)

bash

# Run without installing (recommended)
pipx run opentelemetry-mcp --backend jaeger --url http://localhost:16686

# Or with uvx
uvx opentelemetry-mcp --backend jaeger --url http://localhost:16686

This approach:

✅ Always uses the latest version
✅ No global installation needed
✅ Isolated environment automatically
✅ Works on all platforms

Per Client Integration

Claude Desktop

Configure the MCP server in your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Using pipx (recommended):

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "pipx",
      "args": ["run", "opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Using uvx (alternative):

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "uvx",
      "args": ["opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

For Traceloop backend:

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "pipx",
      "args": ["run", "opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "traceloop",
        "BACKEND_URL": "https://api.traceloop.com",
        "BACKEND_API_KEY": "your_traceloop_api_key_here"
      }
    }
  }
}

Using the repository instead of pipx?

If you're developing locally with the cloned repository, use one of these configurations:

Option 1: Wrapper script (easy backend switching)

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "/absolute/path/to/opentelemetry-mcp-server/start_locally.sh"
    }
  }
}

Option 2: UV directly (for multiple backends)

config.json

{
  "mcpServers": {
    "opentelemetry-mcp-jaeger": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/opentelemetry-mcp-server",
        "run",
        "opentelemetry-mcp"
      ],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Claude Code

Claude Code works with MCP servers configured in your Claude Desktop config. Once configured above, you can use the server with Claude Code CLI:

bash

# Verify the server is available
claude-code mcp list

# Use Claude Code with access to your OpenTelemetry traces
claude-code "Show me traces with errors from the last hour"

Codeium (Windsurf)

Open Windsurf
Navigate to Settings → MCP Servers
Click Add New MCP Server
Add this configuration:

Using pipx (recommended):

config.json

{
  "opentelemetry-mcp": {
    "command": "pipx",
    "args": ["run", "opentelemetry-mcp"],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Using uvx (alternative):

config.json

{
  "opentelemetry-mcp": {
    "command": "uvx",
    "args": ["opentelemetry-mcp"],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Using the repository instead?

config.json

{
  "opentelemetry-mcp": {
    "command": "uv",
    "args": [
      "--directory",
      "/absolute/path/to/opentelemetry-mcp-server",
      "run",
      "opentelemetry-mcp"
    ],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Cursor

Open Cursor
Navigate to Settings → MCP
Click Add new MCP Server
Add this configuration:

Using pipx (recommended):

config.json

{
  "opentelemetry-mcp": {
    "command": "pipx",
    "args": ["run", "opentelemetry-mcp"],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Using uvx (alternative):

config.json

{
  "opentelemetry-mcp": {
    "command": "uvx",
    "args": ["opentelemetry-mcp"],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Using the repository instead of pipx?

config.json

{
  "opentelemetry-mcp": {
    "command": "uv",
    "args": [
      "--directory",
      "/absolute/path/to/opentelemetry-mcp-server",
      "run",
      "opentelemetry-mcp"
    ],
    "env": {
      "BACKEND_TYPE": "jaeger",
      "BACKEND_URL": "http://localhost:16686"
    }
  }
}

Gemini CLI

Configure the MCP server in your Gemini CLI config file (~/.gemini/config.json):

Using pipx (recommended):

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "pipx",
      "args": ["run", "opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Using uvx (alternative):

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "uvx",
      "args": ["opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Then use Gemini CLI with your traces:

bash

gemini "Analyze token usage for gpt-4 requests today"

Using the repository instead?

config.json

{
  "mcpServers": {
    "opentelemetry-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/opentelemetry-mcp-server",
        "run",
        "opentelemetry-mcp"
      ],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686"
      }
    }
  }
}

Prerequisites:

Python 3.11 or higher
pipx or uv installed

Optional: Install globally

If you prefer to install the command globally:

bash

# Install with pipx
pipx install opentelemetry-mcp

# Verify
opentelemetry-mcp --help

# Upgrade
pipx upgrade opentelemetry-mcp

Or with pip:

Terminal

pip install opentelemetry-mcp

Features

Core Capabilities

🔌 Multiple Backend Support - Connect to Jaeger, Grafana Tempo, or Traceloop
🤖 LLM-First Design - Specialized tools for analyzing AI application traces
🔍 Advanced Filtering - Generic filter system with powerful operators
📊 Token Analytics - Track and aggregate LLM token usage across models and services
⚡ Fast & Type-Safe - Built with async Python and Pydantic validation

Tools

Read the full README →View source on GitHub →

Related MCP Servers

View all in Finance & Fintech View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

199

Stargazers on the source repository.

Last commit

2mo ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Opentelemetry MCP Server

The server supports Jaeger, Tempo, Traceloop, and other OpenTelemetry-compatible backends.

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

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CategoryFinance & Fintech

PricingFree

9/10 checks healthy over the last 33d

59Quality signal: Good · 59/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools27/30

Adoption & activity7/15

Community engagement0/10

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Scanned 27d ago via OSV.dev · uvx (PyPI)

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More in 💰 Finance & Fintech →Best MCP servers for Finance & Fintech →Alternatives to Opentelemetry MCP Server →Install in Claude Desktop Install in Cursor Install in VS Code

Opentelemetry MCP Server

Quick Install

Client Config & Setup

Overview

Use cases

Key features

Documentation Overview

OpenTelemetry MCP Server

Table of Contents

Quick Start

Installation

For End Users (Recommended)

Per Client Integration

Features

Core Capabilities

Tools

Related MCP Servers

Adoption & maintenance

Reviews

Frequently Asked Questions about Opentelemetry MCP Server

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Technical Specs & Signals

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Moxie Docs MCP

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Opentelemetry MCP Server

Quick Install

Client Config & Setup

Overview

Use cases

Key features

Documentation Overview

OpenTelemetry MCP Server

Table of Contents

Quick Start

Installation

For End Users (Recommended)

Per Client Integration

Features

Core Capabilities

Tools

Related MCP Servers

Adoption & maintenance

Reviews

Frequently Asked Questions about Opentelemetry MCP Server

AllMCPs Directory Badge

Technical Specs & Signals

Supply-chain signal

Moxie Docs MCP

Own this project?

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