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Glama

Gtin

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?

Disclosed behavioral details beyond annotations: fans out to SEC EDGAR, GDELT→GNews fallback on rate limit/5xx, USPTO soft-fail due to API sunset. Annotations indicate readOnlyHint=true (safe read) and idempotentHint=true, which are consistent.

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

Conciseness5/5

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

The description is dense but not verbose. Every sentence provides necessary information: purpose, examples, behavior details, parameter formats, and sibling differentiation. Front-loads the core purpose with query examples.

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 no output schema, the description explains the return structure (changes grouped by source, total_changes count, pipeworx:// citation URIs). It covers all needed context for a complex multi-source tool with fallback behavior, leaving no major gaps.

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

Parameters4/5

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

Schema description coverage is 100% for all 3 parameters, so baseline is 3. The description adds value by explaining the 'since' parameter formats ('7d', '30d', '3m', '1y') and recommending '30d' or '1m' for typical monitoring, which goes beyond the schema.

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 states 'change feed for a company' with explicit query examples ('What's new with X', 'latest on Y'). It distinguishes from sibling entity_profile by specifying that entity_profile is for static profiles regardless of window.

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?

Provides explicit guidance on when to use this tool ('for a company in the last N days/weeks/months') and when to use the sibling tool entity_profile instead. Includes example queries that clarify the use case.

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

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve a query-routing/research function, with ask_pipeworx and ask_pipeworx_beta being currently identical. The five polymarket_* tools also share boundaries, making it genuinely ambiguous which one to pick for a given betting question.

Naming Consistency2/5

The set mixes verb-style names (forget, recall, subscribe, unsubscribe), noun-style names (entity_profile, recent_changes, bet_research), and brand-prefixed families (ask_pipeworx*, polymarket_*). There is no single verb_noun pattern, and conventions differ across families even though individual families are internally consistent.

Tool Count2/5

With 33 tools, the count is high, and the server is named 'Gtin' yet only two tools relate to barcodes/GTIN — the rest form a sprawling data-research and prediction-market toolkit. Many tools could be consolidated (e.g., the ask_pipeworx variants, the polymarket suite), so the surface feels heavier than its core purpose requires.

Completeness4/5

For the actual domain revealed by the tools — multi-source structured data lookup, entity profiling, prediction-market analysis, and agent memory — the surface is quite complete: it covers query routing, grounded verification, comparisons, research, subscriptions, memory, and feedback loops. However, given the server name 'Gtin', the barcode domain is severely under-covered (only validation and check digit, no lookup or product data), which prevents a perfect score.

Resources