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Glama

Giantbomb

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

A4.9/5.0
Behavior5/5

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

Beyond annotations that declare read-only, open-world, and idempotent behavior, the description adds rich behavioral details: fan-out to multiple sources, fallback logic (GDELT→GNews), USPTO soft-fail, and return structure with 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.

Conciseness4/5

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

The description is comprehensive but slightly verbose with multiple example queries in the opening; a more streamlined opening could improve conciseness without losing clarity.

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?

With no output schema, the description fully explains the return structure (changes[], total_changes, citation URIs) and covers all parameter usage, sources, and fallback behavior, leaving no gaps for the agent.

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?

Although schema coverage is 100%, the description adds significant value by explaining 'since' format with examples (ISO date and relative shorthand), recommending typical values, and clarifying that 'value' accepts ticker or CIK.

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 provides a change feed for a company over a time window, lists example queries, and distinguishes from sibling 'entity_profile' by specifying it returns dynamic changes vs 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 Guidelines5/5

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

The description gives explicit when-to-use guidance with example queries and directly names the alternative 'entity_profile' for static profile needs, leaving no ambiguity about which tool to choose.

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

C2.8/5.0
Disambiguation2/5

Several tool families have blurry boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route into the same underlying catalog with only subtle differences, and the six polymarket_* tools overlap heavily (edge scanning vs. fill-risk vs. arbitrage vs. cross-venue spread). Compounding this, the server is named 'Giantbomb' but only 7 of 38 tools relate to that domain, so an agent cannot predict what this server does from its name.

Naming Consistency3/5

All tool names are snake_case, which helps, but the verb pattern is inconsistent: verb_noun (list_subscriptions, resolve_entity, validate_claim), bare verbs (remember, recall, forget), noun-prefix subjects (entity_profile, pipeworx_feedback, polymarket_edges, recent_changes), and even a versioned name (ask_pipeworx_beta) that breaks its own family's pattern. There is no predictable naming scheme an agent could use to guess a tool's name.

Tool Count2/5

38 tools is well past the 25+ 'too many' threshold, and the count is split across two unrelated concerns: ~7 Giant Bomb tools (several explicitly marked 'API offline as of 2026-05') and ~31 tools for a Pipeworx data-research and Polymarket-trading platform. The number is not merely heavy; it serves no single coherent purpose.

Completeness2/5

For the declared Giant Bomb domain, the surface is incomplete (missing videos, franchises, reviews, people, and other known resource types) and largely dead since the underlying API is offline. The Pipeworx half is comparatively complete with routing, grounded answers, research, memory, subscriptions, and feedback, but that coverage is attached to the wrong server identity and does not fill the gaps in the claimed purpose.

Resources