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Glama

Phoenix Number

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

A4.3/5.0
Behavior4/5

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

Adds behavioral context beyond annotations: fans out to multiple sources, fallback logic, soft-fail for patents, return structure. No contradictions 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?

Well-structured with front-loaded examples. Each sentence adds value, though slightly lengthy; still efficient given the complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description adequately explains return format and error handling (soft-fail for patents). Comprehensive for an aggregation tool.

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%, but description adds meaning with examples (ISO date, relative shorthand) and typical values for the 'since' parameter, aiding correct usage.

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 covering SEC filings, news, and patents, with specific verbs and resource identification. It contrasts with entity_profile, distinguishing it from a sibling tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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

Explicit guidance to use entity_profile instead for static profiles, and example queries are given. Could be slightly more explicit about when not to use this tool, but the sibling list provides context.

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

A3.5/5.0
Disambiguation2/5

Many tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying sources; bet_research, polymarket_edges, and polymarket_arbitrage all find betting opportunities. entity_profile, recent_changes, and compare_entities similarly overlap on company data. The set includes near-duplicates, making misselection likely.

Naming Consistency3/5

All names use snake_case and lowercase, but the verb-noun pattern is inconsistent: some are verb-first (discover_tools, validate_claim), others noun-first (entity_profile, bet_research, pipeworx_trending), and some are noun-noun (polymarket_arbitrage). Prefixes like ask_pipeworx and polymarket_ provide some consistency, but overall naming style is mixed yet readable.

Tool Count2/5

With 32 tools, this is above the comfortable range for a coherent server. The count is bloated by three near-identical ask_pipeworx variants, six polymarket tools, and several overlapping meta-tools. A smaller, more focused set would be more appropriate.

Completeness3/5

As a data-access platform, it covers lookup, grounded answers, deep research, entity comparison, validation, and discovery, plus memory and subscription lifecycle. However, the set is a grab-bag with no unified purpose; there are dead ends like no way to directly call the 5,354 underlying tools except through ask_pipeworx, and the joke tool adds nothing to any workflow. The heterogeneous scope makes it hard to assess true completeness.

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