ai-mldeveloper-tools

AgentOps

by agentops-ai

Access AgentOps data for agent debugging: retrieve project info, trace details, span metrics, and execution traces via a

Provides access to AgentOps observability and tracing data for debugging agent runs, enabling retrieval of project information, trace details, span metrics, and complete execution traces through authenticated API access.

github stars

14

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  • / General purpose MCP workflows

capabilities

    what it does

    Provides access to AgentOps observability and tracing data for debugging agent runs, enabling retrieval of project information, trace details, span metrics, and complete execution traces through authenticated API access.

    about

    AgentOps is an official MCP server published by agentops-ai that provides AI assistants with tools and capabilities via the Model Context Protocol. Access AgentOps data for agent debugging: retrieve project info, trace details, span metrics, and execution traces via a It is categorized under ai ml, developer tools.

    how to install

    You can install AgentOps in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

    license

    MIT

    AgentOps is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

    readme

    AgentOps MCP Server

    smithery badge

    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.

    Usage

    MCP Client Configuration

    Add the following to your MCP configuration file:

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

    Installation

    Installing via Cursor Deeplink

    Install MCP Server

    Installing via Smithery

    To install agentops-mcp for Claude Desktop automatically via Smithery:

    npx -y @smithery/cli install @AgentOps-AI/agentops-mcp --client claude
    

    Local Development

    To build the MCP server locally:

    # Clone and setup
    git clone https://github.com/AgentOps-AI/agentops-mcp.git
    cd mcp
    npm install
    
    # Build the project
    npm run build
    
    # Run the server
    npm pack
    

    Available Tools

    auth

    Authorize using an AgentOps project API key and return JWT token.

    Parameters:

    • api_key (string): Your AgentOps project API key

    get_trace

    Retrieve trace information by ID.

    Parameters:

    • trace_id (string): The trace ID to retrieve

    get_span

    Get span information by ID.

    Parameters:

    • span_id (string): The span ID to retrieve

    get_complete_trace

    Get comprehensive trace information including all spans and their metrics.

    Parameters:

    • trace_id (string): The trace ID

    Requirements

    • Node.js >= 18.0.0
    • AgentOps API key (passed as parameter to tools)

    FAQ

    What is the AgentOps MCP server?
    AgentOps is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
    How do MCP servers relate to agent skills?
    Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
    How are reviews shown for AgentOps?
    This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
    MCP server reviews

    Ratings

    4.510 reviews
    • Shikha Mishra· Oct 10, 2024

      AgentOps is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

    • Piyush G· Sep 9, 2024

      We evaluated AgentOps against two servers with overlapping tools; this profile had the clearer scope statement.

    • Chaitanya Patil· Aug 8, 2024

      Useful MCP listing: AgentOps is the kind of server we cite when onboarding engineers to host + tool permissions.

    • Sakshi Patil· Jul 7, 2024

      AgentOps reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

    • Ganesh Mohane· Jun 6, 2024

      I recommend AgentOps for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

    • Oshnikdeep· May 5, 2024

      Strong directory entry: AgentOps surfaces stars and publisher context so we could sanity-check maintenance before adopting.

    • Dhruvi Jain· Apr 4, 2024

      AgentOps has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

    • Rahul Santra· Mar 3, 2024

      According to our notes, AgentOps benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

    • Pratham Ware· Feb 2, 2024

      We wired AgentOps into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

    • Yash Thakker· Jan 1, 2024

      AgentOps is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.