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Glama

Hunter

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description details fan-out behavior, fallback logic (GDELT→GNews), soft-fails (USPTO PatentsView API sunset), and return structure. Annotations already declare readOnlyHint and idempotentHint, but description adds rich behavioral context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is comprehensive and well-structured with examples first, followed by technical details. Slightly lengthy but efficient for the complexity; no wasted sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite lacking an output schema, the description details the return structure (changes[] grouped by source, total_changes count, citation URIs). For a tool with 3 required params and complex external API logic, the description is fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds significant meaning: explains date format options (ISO vs relative), acceptable values for each parameter, and clarifies that type only supports 'company'. This goes well beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly defines the tool as a 'change feed for a company' with specific query examples. It distinguishes from sibling 'entity_profile' by stating when to use each, achieving full purpose clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance provided: 'Use entity_profile instead when you want the static profile regardless of window.' Also gives examples of typical queries and recommends '30d' or '1m' for monitoring.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation2/5

Several tool clusters have genuinely blurry boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly 'currently matches ask_pipeworx exactly'), ask_pipeworx_grounded, and deep_research all route questions to the same 5,743-tool catalog, and the six Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) heavily overlap on 'should I bet / where is the edge'. The descriptions are detailed and cross-reference each other, but an agent would still struggle to pick correctly among near-duplicates.

Naming Consistency3/5

Everything is uniformly snake_case and readable, but there is no consistent verb_noun pattern: verb_noun (resolve_entity, discover_tools, validate_claim) mixes with noun_noun (domain_search, entity_profile, polymarket_arbitrage), adjective_noun (deep_research, recent_changes), bare verbs (forget, recall, remember), and brand prefixes (pipeworx_*, ask_pipeworx_*). Readable, but patternless.

Tool Count2/5

34 tools is well above the heavy threshold, but the bigger issue is scope incoherence: a server named 'Hunter' dedicates only 3 of 34 tools to Hunter.io email lookup while the remaining 31 belong to an unrelated Pipeworx data-research/prediction-market platform. The count is not earned by a single coherent purpose.

Completeness3/5

The Pipeworx research core is quite thorough: entity resolution, single-lookup, grounded answers, deep research, profiles, comparisons, claim validation, semantic search-within-records, change feeds, subscriptions, and memory form a full research lifecycle. However, the domain is a grab-bag spanning email finding, npm dependency checking, prediction markets, and data research, and the three Hunter.io tools that match the server name are only a thin fragment of the surface.

Resources