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Glama

zenquotes

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

A5/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive behavior; description adds rich behavioral context: parallel fan-out to multiple sources, fallback logic on rate limits/5xx, PatentsView sunset soft-failure, and return shape (changes[] grouped by source, total_changes, pipeworx:// URIs). No contradiction 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 dense but every sentence earns its place: usage phrases, source breakdown, fallback rules, time format, return structure, and sibling differentiation. It is front-loaded with concrete user queries and avoids filler.

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?

For a tool with no output schema, the description fully explains what is returned (structured changes grouped by source, total_changes, citation URIs), how the tool behaves across sources, and when to choose an alternative. It is complete for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds crucial semantic examples: `since` accepts ISO or relative shorthand ('7d', '30d', '3m', '1y') with recommended default '30d'; `value` can be ticker or zero-padded CIK. This enhances the bare schema descriptions.

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 states this is a change feed tool for a company over a recent window, with explicit example phrases like 'What's new with X' and 'latest on Y'. It distinguishes from sibling entity_profile by stating when to use the static profile 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?

Provides explicit usage contexts: natural-language queries about recent company changes, and names the alternative tool (entity_profile) for static profiles outside a window. Also details fallback behavior between GDELT and GNews, and source-specific behavior like USPTO soft-fail.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All zenquotes toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, and the heavy overlap between ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research is mitigated by detailed usage guidance. A few pairs like ai_visibility_check vs scan_competitor_ai_presence or discover_tools vs suggest_questions could confuse an agent, but the descriptions generally draw clear boundaries.

Naming Consistency3/5

Names are all lowercase snake_case and several families are consistent (ask_pipeworx*, polymarket_*), but the overall set mixes styles: verb_noun such as list_quotes and resolve_entity, adjectival noun phrases such as random_quote and today_quote, and bare nouns like entity_profile and recent_changes. The patterns are readable but not predictable.

Tool Count2/5

34 tools is well above the 25+ threshold for 'too many,' and the server name zenquotes suggests a much smaller quote-focused surface. The count is defensible for a broad data gateway, but as a unified server it feels overstuffed with many unrelated feature areas.

Completeness4/5

The combined surface is quite complete for its apparent scope: quote retrieval, query/research, grounding, entity resolution, comparisons, change feeds, subscriptions, memory, and prediction-market analysis are all covered. Minor gaps exist, such as no direct quote search and no explicit tool for fetching a pipeworx:// citation URI.

Resources