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Glama

Currents

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

A4.4/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, and nondestructive. Description adds beyond that: it fans out to multiple sources in one parallel call, mentions GDELT→GNews fallback, and notes the patents API soft-fails. No contradiction.

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 dense but well-structured, front-loading the purpose and query examples. Every sentence serves a purpose, though length could be slightly reduced.

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 all key aspects: sources, fallback, syntax for 'since', return structure (changes[] grouped by source, total_changes count, citation URIs). No output schema, but description provides enough detail on what is returned.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds some context for 'since' (ISO date or relative shorthand) and explains the news fallback, but does not significantly expand beyond schema's own descriptions.

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 uses specific verbs ('change feed') and resources ('SEC EDGAR', 'GDELT→GNews', 'USPTO'), and clearly distinguishes from sibling tool 'entity_profile' by stating when to use that instead.

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 gives example queries ('What's new with X', 'latest on Y'), explains the scope ('in the last N days/weeks/months'), and provides an alternative ('Use entity_profile instead'). Also describes fallback behavior for news sources.

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
Disambiguation2/5

Several tool clusters are hard to tell apart: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers (beta currently behaves exactly like stable), and the six polymarket_* tools plus bet_research all overlap around edge and arbitrage discovery. The descriptions are detailed and help, but an agent must read carefully to avoid misselecting a sibling tool.

Naming Consistency3/5

Names are consistently lowercase snake_case, but the verb/noun ordering is mixed: verb-first names (ask_pipeworx, list_categories, resolve_entity) coexist with noun-first names (polymarket_edges, entity_profile, pipeworx_feedback, recent_alerts) and bare verbs (remember, recall, forget). Readable overall, but the pattern is not systematic.

Tool Count2/5

34 tools is well beyond the 25+ threshold and spans at least six loosely related domains (news, structured data research, prediction markets, AI visibility, memory, subscriptions), making the server feel like a multi-product grab bag. Meta-tools like discover_tools, suggest_questions, and pipeworx_trending add further navigation overhead.

Completeness4/5

Within each bundled domain the lifecycle is well covered: data lookup has routing, grounded mode, deep research, entity resolution, comparison, and claim validation; Polymarket has research, arbitrage, edge, fill-risk, and cross-venue spread tools; memory and subscriptions each have full CRUD-ish flows. Minor gaps like subscription updating or direct article-by-ID fetching are workarounds rather than dead ends.

Resources