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Glama

Us Iso Grid

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral details: fans out to multiple sources, GDELT→GNews fallback, USPTO soft-fail, and structure of returns (changes[] grouped by source, total_changes, citation URIs). No contradictions.

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?

Description is dense but efficient, front-loading purpose with example queries, then detailing sources, parameters, and return format. No redundancy, though slightly long; could benefit from bullet points for 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?

Given the tool has 3 simple parameters, no output schema, and no nested objects, the description provides sufficient context: explains data sources, return structure (changes[], total_changes, URIs), and known limitations (USPTO soft-fail). Competent for an AI agent to invoke.

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 parameters with descriptions (100% coverage). The description adds further guidance: examples for since (ISO date and relative shorthand, recommended '30d'), notes type only supports 'company', and gives examples for value (ticker or CIK). Provides extra clarity 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?

The description clearly states the tool retrieves recent changes for a company (e.g., filings, news, patents) within a time window, using example queries like 'What's new with X' and explicitly distinguishing from sibling tool entity_profile which provides static profiles.

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

Usage Guidelines4/5

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

Provides explicit context for when to use this tool (change feed within a window) and when to use entity_profile instead (static profile regardless of window). Recommends typical since values ('30d' or '1m'), but could be more precise about when not to use this tool (e.g., for real-time news).

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
Disambiguation3/5

There are multiple overlapping query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, discover_tools) that could confuse an agent about which to use. Similarly, several Polymarket tools have overlapping functions. However, descriptions are detailed enough to distinguish most, and energy grid tools are clearly separated by ISO.

Naming Consistency3/5

Tool names consistently use snake_case, but there is no uniform verb_noun pattern. Some start with verbs (ask, compare, scan), others with nouns (entity_profile, recent_alerts), and many use domain prefixes (caiso_, polymarket_). This mixed style reduces predictability.

Tool Count2/5

With 40 tools spanning unrelated domains (AI visibility, energy grids, prediction markets, memory, SEO, subscriptions), the server feels overloaded. A typical focused server would have 3-15 tools; this range indicates scope creep and lack of coherence.

Completeness2/5

The server covers multiple domains but lacks depth in each. For example, energy grid tools cover only three ISOs (CAISO, ERCOT, NYISO), missing many others. The prediction market tools are extensive but incomplete without real-time price updates. The general query tool 'ask_pipeworx' tries to cover everything, but the overall surface is uneven and has notable gaps.

Resources