Agentops Mcp

@AgentOps-AI

About Agentops Mcp

The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.

#observability #tracing #ai #agents

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "agentops-mcp": {
      "command": "npx",
      "args": [
        "agentops-mcp"
      ],
      "env": {
        "AGENTOPS_API_KEY": ""
      }
    }
  }
}

Tools

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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Agentops Mcp?

Agentops Mcp is a Model Context Protocol server that provides access to observability and tracing data for debugging complex AI agent runs. It adds crucial context about where an AI agent succeeds or fails, making it useful for AI developers building and troubleshooting agentic workflows.

How to use Agentops Mcp?

Configure your MCP client (e.g., Claude Desktop) by adding the agentops-mcp server with the command npx agentops-mcp and the environment variable AGENTOPS_API_KEY. You can also install it automatically via Smithery or build it locally from the repository. Use the available tools to authenticate, retrieve traces/spans, and fetch performance metrics.

Features of Agentops Mcp

Authenticate with an AgentOps project API key to get a JWT token
Retrieve project information and configuration settings
Look up trace information by trace ID
Get span details by span ID
Fetch performance metrics for a specific trace or span
Obtain comprehensive trace data including all spans and metrics

Use cases of Agentops Mcp

Debugging why an AI agent fails during a multi-step task
Analyzing performance bottlenecks in agent execution traces
Correlating trace and span metrics to pinpoint errors
Monitoring AI agent runs in production or development environments

FAQ from Agentops Mcp

What tools does Agentops Mcp provide?

It provides tools for authentication (auth), getting project info (get_project), retrieving traces and spans (get_trace, get_span), fetching metrics (get_trace_metrics, get_span_metrics), and getting complete trace data with all spans (get_complete_trace).

What are the requirements to run Agentops Mcp?

Node.js version 18.0.0 or higher and a valid AgentOps API key are required.

How do I install Agentops Mcp?

You can install it via Smithery using the command npx -y @smithery/cli install @AgentOps-AI/agentops-mcp --client claude, or build it locally by cloning the repository and running npm install and npm run build.

How does authentication work?

Use the auth tool with your AgentOps project API key as a parameter. It returns a JWT token used for subsequent API calls.

What observability data can I access?

You can access trace and span information by ID, performance metrics for traces and spans, and a complete trace with all its spans and their metrics.

Frequently asked questions

What tools does Agentops Mcp provide?

It provides tools for authentication (`auth`), getting project info (`get_project`), retrieving traces and spans (`get_trace`, `get_span`), fetching metrics (`get_trace_metrics`, `get_span_metrics`), and getting complete trace data with all spans (`get_complete_trace`).

What are the requirements to run Agentops Mcp?

Node.js version 18.0.0 or higher and a valid AgentOps API key are required.

How do I install Agentops Mcp?

You can install it via Smithery using the command `npx -y @smithery/cli install @AgentOps-AI/agentops-mcp --client claude`, or build it locally by cloning the repository and running `npm install` and `npm run build`.

How does authentication work?

Use the `auth` tool with your AgentOps project API key as a parameter. It returns a JWT token used for subsequent API calls.

What observability data can I access?

You can access trace and span information by ID, performance metrics for traces and spans, and a complete trace with all its spans and their metrics.

Comments

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