The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the MCP Agent Trace Inspector listing page.
npm mcp-agent-trace-inspector package
Local-first, MCP-native observability for agent workflows. Every tool call, prompt transformation, latency, and token count is recorded in a local SQLite database — no cloud account, no API key, no traces leaving your machine. Built specifically for MCP rather than bolted onto a generic LLM proxy.
Tool reference | Configuration | Contributing | Troubleshooting | Design principles
| mcp-agent-trace-inspector | LangSmith / AgentOps | |
|---|---|---|
| Data location | Local SQLite — never leaves your machine | Cloud-hosted; traces sent to external servers |
| Setup | npx one-liner, zero config | Account signup, API key, SDK instrumentation |
| MCP-aware | Native — records tool calls as first-class steps | Generic LLM proxy; MCP structure is opaque |
| Run diffs | Built-in compare_traces diff | Separate paid feature or manual export |
| Cost estimation | Offline tiktoken + configurable pricing table | Requires live API traffic through their proxy |
| Overhead | <5ms per step | Network round-trip per event |
If your traces contain sensitive tool outputs, proprietary prompts, or data that must stay on-device, this is the right tool. If you need cross-team trace sharing or a managed SaaS, use LangSmith.
mcp-agent-trace-inspector stores tool call inputs and outputs locally in a SQLite database. Traces may contain sensitive information passed to or returned from your tools. Review trace contents before sharing dashboard exports. Traces are not automatically transmitted; optional alert webhooks are available.
Add the following config to your MCP client:
To set a custom storage path:
Amp · Claude Code · Cline · Cursor · VS Code · Windsurf · Zed
Enter the following in your MCP client to verify everything is working:
Your client should return a summary showing step count, total tokens, and latency.
trace_start — begin a new trace; returns a trace_id for subsequent callstrace_step — record one tool call step (inputs, outputs, optional token count and latency)trace_end — mark a trace as completedlist_traces — list stored traces with names, statuses, and timestampsget_trace_summary — token totals, step count, latency, and cost estimate for a tracecompare_traces — diff two traces side by side (step counts, tokens, latency)extract_reasoning_chain — extract only reasoning/thinking steps from a traceexport_dashboard — generate a self-contained single-file HTML dashboard with latency waterfallexport_otel — export one or all traces in OpenTelemetry OTLP JSON span formatexport_compliance_log — export the compliance audit log as JSON or CSV, with optional date range filteringconfigure_alerts — configure alert rules on latency, error rate, or cost; fire to Slack or generic webhooksset_retention_policy — set how many days to keep traces (in-memory; must be called before apply_retention)apply_retention — archive traces older than the configured threshold; delete traces past 2x the threshold--db / --db-pathPath to the SQLite database file used to store traces.
Type: string
Default: ~/.mcp/traces.db
--retention-daysAutomatically delete traces older than N days. Set to 0 to disable.
Type: number
Default: 0
--pricing-tablePath to a JSON file containing custom model pricing ($/1K tokens). Overrides the built-in table.
Type: string
--no-token-countDisable tiktoken-based token counting. Traces will omit token usage metrics.
Type: boolean
Default: false
Pass flags via the args property in your JSON config:
Before publishing a new version, verify the server with MCP Inspector to confirm all tools are exposed correctly and the protocol handshake succeeds.
Interactive UI (opens browser):
CLI mode (scripted / CI-friendly):
Run before publishing to catch regressions in tool registration and runtime startup.
See CONTRIBUTING.md for full contribution guidelines.
This plugin is available on:
Search for mcp-agent-trace-inspector.