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Glama

Licenses

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

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, etc.), the description details the multi-source fan-out, fallback mechanisms, and soft-failure scenarios. It adds substantial behavioral context.

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 a single dense paragraph that front-loads examples. While compact, it covers all necessary information without redundancy. A slightly more structured layout could improve readability.

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

Completeness4/5

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

The description covers the tool's complex behavior (multiple sources, fallback, output structure) well. It lacks explicit error or rate limit details, but the annotations and openWorldHint imply some assumptions.

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?

The input schema fully describes parameters, and the description adds value with examples and usage recommendations (e.g., typical `since` values). It enriches what the schema provides.

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 the tool provides a change feed for a company over a time window, fanning out to multiple sources. It distinguishes itself from the sibling `entity_profile` by specifying when to use that instead.

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?

The description includes explicit query examples and a recommendation to use `entity_profile` for static profiles. This provides clear when-to-use and when-not-to guidance.

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 tool groups blur together: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve overlapping discovery/routing purposes. The six polymarket_* tools similarly overlap in edge/arbitrage detection, and the three license tools could be confused despite distinct intents.

Naming Consistency3/5

Naming is mostly snake_case and readable, with coherent families like ask_pipeworx*, polymarket_*, and pipeworx_*. However, conventions vary widely: bare verbs (remember, forget, recall), noun phrases (entity_profile, bet_research), and inconsistent verb prefixes (search_licenses, lookup_license, get_license_text) prevent a predictable pattern.

Tool Count2/5

34 tools is heavy for a single MCP server, and the server is named 'Licenses' while only 3 of 34 tools actually relate to licenses. The bulk is a sprawling data-research, prediction-market, memory, and subscription platform, which makes the count feel bloated and misaligned with the server's stated identity.

Completeness4/5

For the actual (broad) scope, the tool surface is quite thorough: memory CRUD, subscription lifecycle, license search/lookup/text, entity resolution, profiling, comparison, claim validation, research routing, and prediction-market edge analysis are all covered. Minor gaps exist—no subscription update, no license comparison, and ask_pipeworx_beta is a redundant variant—but no major workflow dead-ends.

Resources