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Glama

Phone

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?

Adds significant context beyond annotations: fans out to SEC EDGAR, GDELT→GNews fallback, USPTO with soft-fail. Describes return structure with changes[] and citation URIs. Annotations already indicate read-only, idempotent, non-destructive, and description aligns.

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 examples first, then fan-out explanation, parameter details, return structure, and sibling distinction. Every sentence adds value, though slightly long; could be tightened slightly but still very good.

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?

Complete for a complex tool: covers multiple data sources, fallback behavior, parameter formats, return structure, and sibling differentiation. No output schema, but return structure is described. Handles edge cases like soft-fail for USPTO.

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?

Schema has 100% coverage with descriptions, but description adds more: 'since' accepts ISO or relative shorthand with examples; 'value' explains ticker or CIK; 'type' states only 'company' supported. Provides usage suggestion for 'since'.

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 in a time window, with example queries like 'What's new with X' and explains it fans out to multiple sources. It distinguishes from sibling entity_profile, which is for static profiles.

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 provides example queries and when to use (e.g., monitoring changes). States alternative: 'Use entity_profile instead when you want the static profile'. Also notes 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

A4/5.0
Disambiguation4/5

Tool purposes are mostly distinct, with detailed descriptions differentiating similar-sounding tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research. However, some overlap exists between bet_research and polymarket_* tools, requiring careful reading of descriptions to select the correct one.

Naming Consistency3/5

Tool names mix verb_noun patterns (e.g., resolve_entity, validate_claim) with noun_phrases (e.g., entity_profile, recent_alerts) and occasional inconsistencies like pipeworx_trending or search_within. While most names are readable, the lack of a uniform convention makes it harder to predict tool names.

Tool Count4/5

32 tools cover a broad domain of data retrieval, analysis, prediction markets, and system management. The count is high but reasonable given the extensive feature set, though a more focused set could improve coherence.

Completeness4/5

The tool set covers major needs: data lookup, comparison, validation, monitoring, and memory. Missing CRUD operations for external data are expected as this is a read-centric API, so gaps are minor and don't hinder common tasks.

Resources