A
licenseA
qualityB
maintenanceEnables coding agents to rank repository files, functions, and web search results using Jev-based probabilistic scoring, returning locations and confidence scores for the agent to review.
3
MIT
Hunter-Seeker
Server Details
Rank rows by outcome likelihood: deterministic pattern-detection and top-k prediction for agents
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- Status
- Unhealthy
- Uptime
- 10.2% over 63 days
- OAuth
- Works in Glama
- Last Tested
- Transport
- Streamable HTTP
- URL
TDQS
A4.8/5.0
Scored across 8 tools
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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
8 tool updates
- First observed
hs_context_brief - First observed
hs_describe_capabilities - First observed
hs_explain_drivers - First observed
hs_explain_levers - First observed
hs_model_quality - First observed
hs_poll_task - First observed
hs_provide_dataset - First observed
hs_rank_topk
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