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Rank rows by outcome likelihood: deterministic pattern-detection and top-k prediction for agents

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Unhealthy
OAuth
Works in Glama
Last Tested
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Streamable HTTP
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Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 8 tool updates
    • First observedhs_context_brief
    • First observedhs_describe_capabilities
    • First observedhs_explain_drivers
    • First observedhs_explain_levers
    • First observedhs_model_quality
    • First observedhs_poll_task
    • First observedhs_provide_dataset
    • First observedhs_rank_topk

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TDQS

A4.8/5.0
Disambiguation5/5

Each tool addresses a distinct stage of the workflow: capabilities, data intake, ranking, polling, driver explanation, lever explanation, model diagnostics, and summary generation. The ranking_ref dependency creates a clear pipeline but no purpose overlap.

Naming Consistency5/5

All tools use the hs_ prefix and snake_case with clear verb-noun phrasing (hs_provide_dataset, hs_explain_drivers, hs_model_quality). Naming is uniform and immediately indicates each tool's function.

Tool Count5/5

8 tools is well within the ideal 3–15 range and maps perfectly to the ranking workflow: describe, provide data, rank, poll, then interpret/diagnose/summarize. No redundant or unnecessary tools.

Completeness5/5

The surface covers the full analysis lifecycle: contract understanding (hs_describe_capabilities), data ingestion (hs_provide_dataset), execution and polling (hs_rank_topk, hs_poll_task), interpretation (hs_explain_drivers, hs_explain_levers), trust (hs_model_quality), and portable handoff (hs_context_brief). No obvious gaps or dead ends.

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