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Agentops Mcp

@AgentOps-AI

代码仓库

关于 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

基本信息

分类

开发工具

传输方式

stdio

发布者

AgentOps-AI

提交者

koreandinner

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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.

评论

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