ai-mldeveloper-tools

AgentOps

agentops-ai

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.

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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 64 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

    Use Cases

    Extended AI Capabilities

    Add new capabilities to Claude beyond text generation

    Example

    Access external data sources, execute code, interact with tools and services

    Transform Claude from chatbot to action-taking agent

    Context Enhancement

    Provide Claude with access to relevant context and data

    Example

    Load project documentation, access knowledge bases, query databases

    Get more accurate, context-aware responses

    Workflow Automation

    Automate multi-step workflows combining AI and external tools

    Example

    Research → Summarize → Create document → Send notification

    Complete complex tasks end-to-end without manual steps

    Implementation Guide

    Prerequisites

    • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
    • Basic understanding of MCP architecture and capabilities
    • Access credentials for integrated services (if required)
    • Willingness to experiment and iterate on configuration

    Time Estimate

    15-60 minutes depending on server complexity

    Installation Steps

    1. 1.Install MCP server: npm install -g [package-name] or via GitHub
    2. 2.Add server configuration to ~/.claude/mcp.json
    3. 3.Provide required credentials and configuration
    4. 4.Restart Claude Desktop to load new server
    5. 5.Test basic functionality with simple prompts
    6. 6.Explore capabilities and experiment with use cases
    7. 7.Document successful patterns for reuse

    Troubleshooting

    • MCP server not loading: Check config syntax, verify installation
    • Connection errors: Check network, firewall, credentials
    • Feature not working: Read server docs, check required parameters
    • Performance issues: Monitor resource usage, check for network latency
    • Conflicts with other servers: Check port assignments, namespace collisions

    Best Practices

    ✓ Do

    • +Read server documentation thoroughly before setup
    • +Start with simple use cases to validate functionality
    • +Test in non-production environment first
    • +Monitor resource usage and performance
    • +Keep servers updated for bug fixes and new features
    • +Document configuration for team members
    • +Use environment variables for sensitive configuration

    ✗ Don't

    • Don't grant overly permissive access to MCP servers
    • Don't skip reading security considerations in docs
    • Don't expose sensitive data without proper controls
    • Don't run untrusted MCP servers without code review
    • Don't ignore error messages—investigate root cause

    💡 Pro Tips

    • Combine multiple MCP servers for powerful workflows
    • Create custom MCP servers for your specific needs
    • Share successful configurations with team
    • Use MCP inspector for debugging
    • Join MCP community for tips and troubleshooting

    Technical Details

    Architecture

    Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

    Protocols

    • Model Context Protocol (MCP)
    • JSON-RPC 2.0
    • stdio or HTTP transport

    Compatibility

    • Claude Desktop
    • Cursor IDE
    • Custom MCP clients

    When to Use This

    ✓ Use When

    Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

    ✗ Avoid When

    Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

    Integration

    • Tool composition: Chain multiple MCP tools in workflows
    • Context augmentation: Provide AI with relevant external data
    • Action delegation: Let AI execute tasks on external systems
    • Bidirectional sync: Keep AI context and external systems in sync

    Discussion

    Product Hunt–style comments (not star reviews)
    • No comments yet — start the thread.

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    Ratings

    4.664 reviews
    • Isabella Smith· Dec 28, 2024

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

    • Sakura Liu· Dec 24, 2024

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

    • Olivia Tandon· Dec 24, 2024

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

    • Charlotte Jackson· Dec 20, 2024

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

    • Pratham Ware· Dec 4, 2024

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

    • James Perez· Dec 4, 2024

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

    • Neel Rahman· Dec 4, 2024

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

    • Ishan Ghosh· Nov 23, 2024

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

    • Omar Zhang· Nov 23, 2024

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

    • Neel Okafor· Nov 19, 2024

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

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