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Glama

trivia

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

A4.9/5.0
Behavior5/5

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

Even with readOnlyHint, openWorldHint, and idempotentHint annotations, the description adds substantial behavioral context: it discloses the multi-source fan-out, the GDELT→GNews fallback on rate-limit/5xx, the USPTO soft-failure due to PatentsView API sunset, and the return shape (changes[], total_changes, citation URIs). No annotation contradiction.

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 a single dense paragraph that front-loads natural-language queries and then packs source behavior, fallback logic, date formats, and output contract efficiently. No sentences are wasted; it is long but every sentence carries signal.

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?

Given the tool's complexity (multiple sources, fallback logic, date parsing, no output schema), the description is remarkably complete. It states what is returned, how sources are grouped, how failures are handled, and when to choose a sibling tool. This is sufficient for an agent to select and invoke the tool correctly.

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?

The input schema already covers all three parameters with descriptions (100% coverage). The description adds meaningful syntax examples for since ('2026-04-01', '7d', '30d', '3m', '1y') and value (ticker or zero-padded CIK), plus a recommendation to use '30d' or '1m' for typical monitoring. This goes beyond the 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?

The description clearly identifies the tool as a company change feed over a time window, with specific trigger phrases like 'What's new with X' and 'updates on Acme'. It names concrete data sources (SEC EDGAR, GDELT/GNews, USPTO) and differentiates from entity_profile, which is the static-profile sibling.

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?

It provides explicit when-to-use examples and an explicit alternative: 'Use entity_profile instead when you want the static profile...'. It also explains the one-call parallel fan-out behavior and the fallback from GDELT to GNews, which helps the agent decide when this tool is appropriate.

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

A3.6/5.0
Disambiguation2/5

Several tools have notably unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and polymarket_edges, polymarket_arbitrage, and bet_research heavily overlap in surfacing betting opportunities. Entity_profile, recent_changes, and compare_entities also share overlapping research scope, making misselection likely.

Naming Consistency3/5

Most names use snake_case and a roughly readable verb_noun style (resolve_entity, compare_entities, list_categories), but conventions vary: some are bare nouns (entity_profile), some are plain verbs (remember, forget), and ask_pipeworx/pipeworx_* break the pattern. It is readable overall, but not a consistent scheme.

Tool Count2/5

34 tools is far more than the 'trivia' name implies, and most of them (Pipeworx research, Polymarket analysis, memory, subscriptions) are unrelated to trivia. The set reads as an entire platform bundled together rather than a purpose-scoped server.

Completeness2/5

For the stated trivia purpose, the surface is missing core lifecycle features like quiz sessions, answer validation, or scoring; the few trivia tools are just category/reference lookups. As a general data/research server it is broad, but there are significant gaps and no coherent domain model tying the tools together.

Resources