AgentOps MCP Server

AgentOps MCP Server – Observability for AI Agents

The AgentOps MCP server addresses a critical pain point in modern AI‑driven workflows: the lack of actionable telemetry when an autonomous agent behaves unexpectedly. By exposing observability and tracing data through the Model Context Protocol, it gives developers a unified channel to inspect where an agent succeeds or fails, without leaving their AI‑assistant environment.

At its core, the server offers a set of tools that map directly to AgentOps’ tracing API. Developers can authenticate with an API key, then request detailed trace or span information by ID. The tool aggregates all spans and metrics for a single trace, enabling a holistic view of the agent’s execution path. This level of insight is invaluable when debugging complex chains, diagnosing performance bottlenecks, or validating that an agent’s logic aligns with business rules.

Key capabilities include:

• Seamless integration: The MCP client configuration is straightforward, allowing the server to be launched via a simple command line or through Smithery’s automatic installer.
• Fine‑grained data access: Tools such as and let developers drill down to individual operations, while provides a full end‑to‑end view.
• Secure authentication: The tool issues a JWT token based on the supplied project key, ensuring that only authorized requests reach AgentOps.
• Node.js compatibility: Built on Node ≥ 18, the server can be run locally or deployed in a containerized environment.

Typical use cases span from debugging an agent that intermittently misclassifies user intent to monitoring latency across a multi‑step workflow. By querying trace data directly from the AI assistant, developers can quickly pinpoint problematic spans and adjust prompts or tool calls on the fly. In production settings, this observability layer can feed into alerting systems or dashboards, providing continuous visibility into agent health.

What sets AgentOps apart is its tight coupling with the AgentOps platform, which already offers rich telemetry and metrics. The MCP server simply exposes that data in a format that AI assistants understand, turning raw logs into actionable context. This integration eliminates the need for separate monitoring tools, streamlines debugging cycles, and ultimately leads to more reliable autonomous agents.