Skip to main content
Glama

Rubygems

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

A4.8/5.0
Behavior5/5

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

Description adds value beyond annotations (readOnlyHint, etc.) by detailing parallel fan-out, GDELT→GNews fallback, USPTO soft-fail, and output structure (changes[] grouped by source, total_changes count, citation 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured: starts with query examples, explains functionality, then details each data source and parameter behavior. Each sentence adds value, though length is slightly above average. Still efficient for the complexity.

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?

Despite no output schema, description explains return format (structured changes grouped by source, total_changes count, citation URIs). Covers multiple sources, fallback, and soft-fail behavior. Provides complete contextual information for an agent to understand the tool's capabilities and limitations.

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% with descriptions, but the description adds extra clarity: explains that 'since' accepts ISO date or relative shorthand (with examples) and that 'value' can be ticker or CIK. This is helpful but not essential given thorough schema.

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 it provides a change feed for a company across SEC, GDELT/GNews, and USPTO, and contrasts with sibling entity_profile. It uses specific verb-resource combinations and matches the query examples from the description.

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?

Explicitly tells when to use this tool (for recent changes) and when to use entity_profile instead (for static profile regardless of window). Also describes fallback behavior and data sources, providing clear context.

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

All Rubygems toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes (ask_pipeworx vs ask_pipeworx_beta are currently identical; ai_visibility_check vs scan_competitor_ai_presence; discover_tools vs suggest_questions; validate_claim vs ask_pipeworx_grounded; multiple polymarket tools). The wide mix of unrelated domains (RubyGems, Pipeworx data, memory, subscriptions) makes it hard for an agent to know which tool to pick.

Naming Consistency2/5

All tools use snake_case, but there is no consistent verb_noun pattern. Names include bare verbs (remember, recall, forget, subscribe), nouns (polymarket_edges, pipeworx_feedback), noun-first composites (ai_visibility_check, bet_research), and brand names (ask_pipeworx). The 'pipeworx_' prefix appears on only a few tools, adding inconsistency.

Tool Count2/5

36 tools is excessive for a server named Rubygems, especially since only 5 tools (search_gems, get_gem, get_versions, get_dependencies, get_reverse_dependencies) actually relate to RubyGems. The rest cover unrelated domains, making the count feel bloated and unfocused.

Completeness2/5

The RubyGems cluster is arguably complete for basic lookups (search, metadata, versions, dependencies, reverse dependencies), but it is buried among 31 unrelated tools that have no coherent domain. The server fails to fully cover either RubyGems (no way to browse all gems, no yanked versions, no gem download/content) or any other single purpose, leaving the surface incomplete and inconsistent.

Resources