Agent Observability
Agent Observability provides developers with deep visibility into LLM workflows by tracking spans, execution costs, and token usage. Use obs_event_log and obs_trace_get to debug agent logic, while obs_metrics_summary and obs_anomaly_scan identify latency spikes, cost outliers, and failure patterns. This server enables precise monitoring and incident response for complex agentic systems.
How to pay
Subscribe
$9/month
Predictable monthly cost with included usage. Best for steady, high-volume traffic.
- Unlimited tools within plan limits
- One API key, billed once a month
- Cancel any time
Overview
The Agent Observability MCP server provides a standardized interface for logging, tracing, and analyzing the execution of LLM-based agents. By capturing granular data on model calls, tool usage, and internal processing, it enables developers to move beyond black-box debugging to understand exactly how their agents perform in production.
Key Capabilities
- obs_event_log: Records execution spans including metadata, token usage, and costs for every step of the agent lifecycle.
- obs_trace_get: Reconstructs the full chronological timeline of a specific execution to pinpoint where failures or latency bottlenecks occur.
- obs_events_search: Queries historical logs using filters like project, status, model type, and time windows to isolate problematic events.
- obs_metrics_summary: Aggregates performance data, providing p50/p95/p99 latency, error rates, and total token expenditure.
- obs_anomaly_scan: Automatically identifies operational risks such as excessive retry loops, cost spikes, and performance regressions.
- obs_projects_list: Provides a high-level health status of all tracked projects, including error counts and storage utilization.
Use Cases
- Debugging Latency: A developer uses obs_trace_get to investigate a specific request that exceeded the 5-second threshold, identifying an inefficient database tool call.
- Cost Optimization: An engineer runs obs_metrics_summary to identify which specific agent operations are consuming the most tokens, allowing for targeted prompt engineering improvements.
- Continuous Monitoring: An SRE utilizes obs_anomaly_scan in a CI/CD pipeline to automatically flag critical error rate spikes before they impact end-users.
- Root Cause Analysis: A developer performs an obs_events_search after a production outage to filter all "error" status events across a specific deployment window.
Who This Is For
This server is designed for AI engineers, backend developers, and machine learning operations (MLOps) professionals who need deep visibility into agentic workflows. It is intended for technical users comfortable with diagnostic data and performance monitoring who require empirical evidence to optimize agent reliability and cost-efficiency.