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Glama

Quran

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
Behavior4/5

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

Annotations declare readOnlyHint=true and destructiveHint=false, so the tool is safe. The description adds valuable context: it fans out to SEC EDGAR, GDELT→GNews fallback, and USPTO (with soft-fail note). This goes beyond what annotations provide.

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 well-structured: it starts with concrete example queries, then states the core function, then details sources and parameter format, and ends with an alternative tool. Every sentence contributes meaning without redundancy.

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?

The description covers all key aspects: source fan-out, fallback behavior, parameter acceptance, return structure (changes[], total_changes, citation URIs), and a pointer to an alternative tool. No output schema exists, but the description compensates adequately.

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 coverage is 100%—all parameters have descriptions. The description further clarifies the `since` parameter format with examples (ISO date and relative shorthand). This adds value beyond the schema, especially for usage guidance.

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's purpose as a change feed for a company in a recent time window, with examples ('What's new with X', 'latest on Y'). It also distinguishes itself from entity_profile, which focuses on 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 Guidelines5/5

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

The description explicitly indicates when to use this tool (for recent changes) and provides an alternative ('Use entity_profile instead when you want the static profile'). It also explains the underlying fan-out behavior and fallback logic.

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

A3.9/5.0
Disambiguation2/5

Several tools have overlapping mandates: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all perform routed research, while ai_visibility_check and scan_competitor_ai_presence overlap, and the Polymarket tools aside from bet_research are closely related. The descriptions are detailed, but an agent could easily select the wrong research or data-retrieval tool.

Naming Consistency4/5

The set largely follows a clear snake_case verb_noun pattern (get_ayah, list_surahs, search_quran, resolve_entity, validate_claim). There are minor deviations like entity_profile, recent_alerts, and pipeworx_trending, but no mixed casing or chaotic naming.

Tool Count2/5

35 tools is far too many for a server named 'Quran': only four tools actually serve Quran lookups, while the other 31 are a general-purpose data, prediction-market, and memory suite. The count feels like two or three unrelated servers merged into one, which does not match the stated purpose.

Completeness3/5

The four Quran tools cover the core list/get/search operations reasonably well, so that subdomain has no major dead ends. However, as a Quran server the surface is diluted by unrelated tools, and as a general-purpose server the mixed scope makes it hard to call the tool set complete for any single clear domain.

Resources