Step-by-step observability for MCP agent workflows
{
"mcpServers": {
"io-github-dbsectrainer-mcp-agent-trace-inspector": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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Step-by-step observability for MCP agent workflows
Is it safe?
No package registry to scan.
No authentication — any process on your machine can connect.
License not specified.
Is it maintained?
Last commit 2 days ago.
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
No automated test available for this server. Check the GitHub README for setup instructions.
No known vulnerabilities.
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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:
{
"mcpServers": {
"trace-inspector": {
"command": "npx",
"args": ["-y", "mcp-agent-trace-inspector@latest"]
}
}
}
To set a custom storage path:
{
"mcpServers": {
"trace-inspector": {
"command": "npx",
"args": [
"-y",
"mcp-agent-trace-inspector@latest",
"--db=~/traces/my-project.db"
]
}
}
}
Amp · Claude Code · Cline · Cursor · VS Code · Windsurf · Zed
Enter the following in your MCP client to verify everything is working:
Start a trace called "test-run", then list the files in the current directory, then end the trace and show me the summary.
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 timestamps