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Glama

Ygoprodeck

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

A5/5.0
Behavior5/5

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

Annotations already indicate safe read operation (readOnlyHint, openWorldHint, etc.). Description adds valuable context: fan-out to multiple sources, fallback mechanism for news (GDELT preferred, GNews when rate-limited), soft-fail for USPTO, and return structure with changes grouped by source and citation URIs.

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?

Single well-structured paragraph with example queries, functionality, parameter details, and sibling distinction. Every sentence adds value 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?

Covers purpose, usage, parameters, return structure, and fallback behaviors. Adequate for a 3-parameter tool without an output schema, as description describes the output format.

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 description coverage is 100%, but description adds beyond schema e.g., 'since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y")' and 'value: Ticker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").' Provides practical usage tips.

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 defines the tool as a change feed for a company, listing example queries and specifying sources (SEC EDGAR, GDELT→GNews, USPTO). It also distinguishes from sibling tool entity_profile, stating when to use each.

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?

Explicit guidance on when to use this tool vs. entity_profile ('Use entity_profile instead when you want the static profile...'). Also provides example use cases and parameter recommendations like 'Use "30d" or "1m" for typical monitoring.'

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

A3.7/5.0
Disambiguation3/5

Many tools have distinct purposes, but the cluster of Pipeworx tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are very similar, causing potential confusion. The memory tools (remember, recall, forget) also add some overlap.

Naming Consistency3/5

All tool names use lowercase with underscores, which is consistent. However, the similar Pipeworx tools have confusingly similar names (ask_pipeworx vs ask_pipeworx_grounded vs ask_pipeworx_beta), and the naming does not clearly distinguish their differences.

Tool Count2/5

With 34 tools, the set is too large for a server ostensibly focused on Yu-Gi-Oh! cards. The majority of tools are unrelated domain-agnostic data tools, making the count feel bloated and unfocused.

Completeness2/5

The Yu-Gi-Oh! card tools are limited to lookup and search, missing obvious operations like creating or updating cards. The unrelated data tools, while many, do not form a coherent set for a single purpose, leaving gaps in both directions.

Resources