agent-trace-mcp
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- AlicenseAqualityBmaintenanceEnables local-first observability for MCP agent workflows by recording tool calls, prompts, latency, and token usage in a local SQLite database, and provides tools for inspecting, comparing, exporting, and generating dashboards from traces.1320 npmMIT
- FlicenseNot gradedqualityCmaintenanceEnables live tracing and observability of MCP tool calls, persisting execution traces to MongoDB and streaming them in real time to a dashboard.-
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to self-report every tool call, token usage, decision, and session grade into a local SQLite store, so users can trace execution, audit reasoning, and see per-session costs in a local dashboard. Also supports proxy mode to intercept and trace MCP tool invocations for agents that cannot self-report.35 npm2MIT
- AlicenseNot gradedqualityDmaintenanceProvides unified AI agent observability including tracing, cost tracking, performance monitoring, anomaly detection, and audit trails via MCP.32 npmMIT
- FlicenseNot gradedqualityDmaintenanceRecords all MCP tool interactions in a centralized ledger, enabling developers to trace, replay, inspect, and audit AI agent workflows.-
- AlicenseNot gradedqualityCmaintenanceEnables recording and analyzing AI agent execution traces, including event logging, metric computation, loop detection, and JSON export for debugging agent behavior.MIT
TDQS
Scored across 7 tools
Each tool has a distinct purpose: recording, listing runs, retrieving a run, filtering failures, computing token costs, finding slowest tools, and checking status. No overlaps or ambiguity.
All tools follow the consistent pattern of 'trace_' prefix plus a descriptive verb or noun phrase, using snake_case throughout. This makes the tool set predictable and easy to navigate.
With 7 tools, the server is well-scoped for tracing functionality—covering recording, querying, and analysis without excess or deficiency.
The surface covers core tracing needs: record, list, get, filter failures, cost, performance, and status. Minor gaps like clearing or deleting runs exist but are not critical for typical usage.