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Glama

Google_analytics

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

The description goes beyond the readOnly/idempotent/destructive annotations by revealing the fallback chain (GDELT preferred, GNews when rate-limited or 5xx), the USPTO soft-fail due to PatentsView sunset, and the return format (changes[] grouped by source + total_changes + citation URIs). No contradictions with annotations.

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 long but dense and well-organized. It front-loads common query phrasings, then delivers source behavior, fallbacks, date formats, and return shape without redundancy. Every clause adds operational value.

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 there is no output schema, the description adequately explains the return structure (changes[], total_changes, pipeworx:// citation URIs) and source-specific failure modes. It also names the alternative tool for different user needs, making the context complete for a complex multi-source tool.

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 already covers all 3 parameters with 100% coverage, providing a strong baseline of 3. The description adds practical semantics with relative shorthand examples for 'since' ('7d', '30d', '3m', '1y'), example values for ticker/CIK, and typical usage guidance ('Use 30d or 1m for typical monitoring'), bringing it to a 4.

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 that the tool returns a change feed for a company over a recent window, fanning out to SEC EDGAR, GDELT/GNews, and USPTO. This is a specific verb+resource with clear scope, and it distinguishes recent_changes from sibling tools like entity_profile or compare_entities.

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 when-to-use guidance with natural-language triggers like 'What's new with X' and calls out an alternative: 'Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.' This clearly delineates use cases.

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

A3.5/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ask_pipeworx_grounded and deep_research both route queries to the same 5,529 tools, and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates. Only the small ga_* subset is clearly distinct.

Naming Consistency2/5

Conventions are mixed: GA tools use a ga_ prefix, but the majority use arbitrary names like ask_pipeworx, deep_research, remember, scan_dependency, and polymarket_arbitrage. No consistent verb_noun pattern across the set.

Tool Count2/5

35 tools is heavy, and the vast majority (31) are unrelated Pipeworx utilities rather than Google Analytics functionality. Only 4 tools actually serve the stated GA purpose, making the count inappropriate for the server name.

Completeness2/5

For Google Analytics, the surface is minimal: list properties, metadata, realtime, and run report—no property management, user management, or data mutation. As a Pipeworx toolkit it's broad but lacks full lifecycle coverage for any single domain.

Resources