traceai operator

Development teams struggle to gain visibility into local tool calls, MCP interactions, and agentic workflows without complex cloud infrastructure. Local observability often feels like a black box, leading to silent tool failures or unoptimized agent behavior.

What it does

• CLI Management handles installation, local Go builds, and versioning for the TraceAI binary across platforms.
• MCP Proxying wraps existing Model Context Protocol servers to capture and log JSON-RPC traffic without breaking protocol communication.
• Local Telemetry manages SQLite-backed storage for tool events, function calls, and agent metrics with configurable retention.
• Analysis & Reporting generates rankings for tool usage, failure rates, call sequences, and retry patterns directly from the terminal.
• Health & Monitoring runs diagnostic checks, status reports, and JSON-based metrics smoke tests to verify collector integrity.

How it works

1. Bootstrap the environment by installing via npm, Go, or building the traceai binary from source.
2. Instrument workflows by replacing standard MCP commands with the proxy or integrating the Go SDK.
3. Analyze captured data using commands like top-tools, high-failures, or call-seq to identify bottlenecks.
4. Export results to CSV or JSON formats for external reporting or startup the local Dashboard for visual debugging.

Frameworks & tools

Built for Go 1.25+ and Node.js 18+. Integrates with the Model Context Protocol (MCP), SQLite for local storage, and provides HTTP/gRPC middleware support.

Why this beats prompting it yourself

Writing reliable CLI wrappers for telemetry requires strict handling of stdout/stderr to avoid polluting protocol channels. This skill encodes the specific flag ordering, database locking logic, and capture-mode privacy defaults needed to operate TraceAI safely without manual trial and error.

Use cases

• Debugging MCP server communication by proxying and logging local tool calls.
• Auditing agent performance to identify high-failure tools or zero-call redundancies.
• Setting up local observability dashboards for development environments without external dependencies.
• Automating release validation by running health checks and metric exports post-build.

Known limitations

Does not support long-running processes unless explicitly requested. Database cleanup is only effective if retention days or byte limits are pre-configured. Standard output is reserved for protocol data during proxying.