Access AgentOps data for agent debugging: retrieve project info, trace details, span metrics, and execution traces via a
by 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
Provide Claude with access to relevant context and data
Example: Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
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
Time estimate: 15-60 minutes depending on server complexity
Prerequisites
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Strong directory entry: AgentOps surfaces stars and publisher context so we could sanity-check maintenance before adopting.
AgentOps is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
AgentOps is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
I recommend AgentOps for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
AgentOps is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: AgentOps is the kind of server we cite when onboarding engineers to host + tool permissions.
We evaluated AgentOps against two servers with overlapping tools; this profile had the clearer scope statement.
We evaluated AgentOps against two servers with overlapping tools; this profile had the clearer scope statement.
Useful MCP listing: AgentOps is the kind of server we cite when onboarding engineers to host + tool permissions.
AgentOps has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
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Claude Desktop configuration
{
"mcpServers": {
"agentops-mcp": {
"command": "npx",
"args": [
"agentops-mcp"
],
"env": {
"AGENTOPS_API_KEY": ""
}
}
}
}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.
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.
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.
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.
Add the following to your MCP configuration file:
{
"mcpServers": {
"agentops-mcp": {
"command": "npx",
"args": ["agentops-mcp"],
"env": {
"AGENTOPS_API_KEY": ""
}
}
}
}
To install agentops-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @AgentOps-AI/agentops-mcp --client claude
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
authAuthorize using an AgentOps project API key and return JWT token.
Parameters:
api_key (string): Your AgentOps project API keyget_traceRetrieve trace information by ID.
Parameters:
trace_id (string): The trace ID to retrieveget_spanGet span information by ID.
Parameters:
span_id (string): The span ID to retrieveget_complete_traceGet comprehensive trace information including all spans and their metrics.
Parameters:
trace_id (string): The trace IDSteps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ 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.
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