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Glama

Intact

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?

Annotations indicate read-only, idempotent, non-destructive. Description adds: fans out to multiple sources, fallback logic, soft-fail for USPTO, and return structure with sources, counts, and URIs.

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?

Single dense paragraph, front-loaded with use cases, efficient in wording, covers purpose, behavior, return structure, and alternatives without redundancy.

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?

Given complexity (multi-source, fallback, time windows, no output schema), description provides all needed context: what it returns, how fallback works, when not to use it (entity_profile).

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 covers all 3 params with descriptions; description adds concrete examples (ISO vs relative dates), entity types (ticker/CIK), and usage recommendations. Adds value beyond 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?

Description starts with concrete example queries and clearly states it's a change feed for a company in a time window via one parallel call. It explicitly distinguishes from sibling 'entity_profile'.

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: use 'entity_profile' for static profiles. Also explains fallback behavior (GDELT to GNews) and parameter suggestions (e.g., '30d' 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

A4/5.0
Disambiguation3/5

Most tools have distinct purposes, but ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research form a confusing cluster—especially since ask_pipeworx_beta is currently identical to ask_pipeworx. The Polymarket tools are highly specialized and mostly separable, and interaction_count/find_interactions have clear but overlapping scopes.

Naming Consistency4/5

The dominant pattern is verb_noun snake_case (ask_pipeworx, compare_entities, resolve_entity, validate_claim), which is predictable and readable. There are some noun-style names like entity_profile, recent_changes, and interaction_count, plus brand-prefixed families like pipeworx_* and polymarket_*, but the conventions are consistent enough within families.

Tool Count2/5

33 tools is beyond the 25+ threshold and the set spans several unrelated domains—molecular interactions, npm dependency scanning, llms.txt generation, AI brand visibility, and prediction-market arbitrage—making it feel like multiple servers merged into one. Several niche tools could be consolidated or split into separate MCP servers, and ask_pipeworx_beta adds redundancy.

Completeness4/5

Core workflows are very well covered: lookup/grounded answering/deep research, tool discovery, entity resolution, profiles and comparisons, claim validation, memory CRUD, subscription lifecycle, and prediction-market analysis from edge detection to fill-risk. Minor gaps include no direct fetch tool for pipeworx:// citation URIs and some soft-failing data sources, but agents can work around those.

Resources