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Glama

Google_calendar

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

A4.5/5.0
Behavior5/5

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

Annotations declare readOnlyHint and idempotentHint true. Description adds rich behavioral context: multi-source fan-out (SEC, GDELT, GNews, USPTO), fallback mechanism, API sunset warning, and return structure. 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 fairly detailed but front-loaded with common usage examples. Every sentence adds value, though slightly verbose (e.g., mentioning soft-fail details). Could be trimmed slightly without losing clarity.

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?

No output schema, but description sufficiently explains return structure (changes[] grouped by source, total_changes count, URIs). Covers multi-source behavior, fallback, and limitations. Adequate for moderate complexity 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?

Schema covers 100% of parameters. Description adds value beyond schema with examples for 'since' (ISO date, relative shorthand, recommendation), explains 'value' accepts ticker or CIK, and clarifies 'type' only supports 'company'.

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 clearly identifies the tool as a change feed for a company over a time window, using specific verbs ('get recent changes') and resource ('company change feed'). It distinguishes from sibling tool 'entity_profile' which provides a static profile.

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 usage examples and recommends when to use 'entity_profile' instead. However, it does not state when NOT to use this tool (e.g., for real-time news), though the sibling list suggests alternatives like 'ask_pipeworx'.

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

B3.3/5.0
Disambiguation1/5

The tool set is a mix of 5 Google Calendar tools and 31 unrelated Pipeworx tools (e.g., ask_pipeworx, deep_research, entity_profile). Even within Pipeworx, tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded have highly overlapping purposes, making it very difficult for an agent to distinguish which tool to use.

Naming Consistency1/5

Naming conventions are chaotic. The Google Calendar tools follow a consistent gcal_ prefix, but the rest use various patterns: pipeworx_*, polymarket_*, single words (remember, recall), and others (bet_research, scan_dependency). There is no overarching pattern.

Tool Count1/5

With 36 tools, the count is high, but only 5 are relevant to Google Calendar. The remaining 31 tools are from a completely different domain (Pipeworx data access, Polymarket betting, etc.), making the tool count severely inappropriate for the server's stated purpose.

Completeness2/5

For Google Calendar, the tools provide basic CRUD (create, get, list, search, list_calendars) but lack update and delete functionality. The vast number of unrelated tools does not compensate for these gaps. The overall surface is incomplete for the calendar domain.

Resources