abhishekash-mcp-trace

Purpose: Inspecting AI agent runs, traces, approvals, and token costs without manually searching observability data.

Inferred · not functionally tested

Audience: AI agent developers and observability engineers

Inferred · not functionally tested

Functions: monitoring, analytics

Inferred · not functionally tested

Interfaces: API: indicated (inferred, not tested) · MCP: indicated (inferred, not tested) · CLI: indicated (inferred, not tested) · Self-hosting: indicated (inferred, not tested)

Recorded constraints: pricing: open_source · license: MIT · platforms: CLI · deployment: cli, self_hosted, api_only

Constraint provenance is unknown; confirm requirements with the publisher.

Record sources: pypi.org · github.com. These links do not verify the individual claims.

abhishekash-mcp-trace sits in PulseGate's LLM & agent tracing category. Inferred · not functionally tested: It focuses on inspecting AI agent runs, traces, approvals, and token costs without manually searching observability data. Inferred · not functionally tested: abhishekash-mcp-trace is an open-source project aimed at AI agent developers and observability engineers. Basis unknown · not verified: The project is open source (MIT). Basis unknown · not verified: It ships for the command line and API, and it can be self-hosted.

Abhishek Ash builds and maintains abhishekash-mcp-trace, and it first shipped in 2026. The project is developed in the open on GitHub with 13 commits in the last 90 days. Inferred · not functionally tested: Among its 6 catalogued features are trace querying, run inspection, and span trees. Inferred · not functionally tested: Catalogued interfaces include an MCP server.

Summary written by a language model from the project’s public pages.