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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").

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds significant context: fans out to multiple sources, fallback logic, date format support (ISO/relative), return structure with citations. No contradiction 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?

The description is detailed but front-loaded with examples, making it easy to grasp. Every sentence adds value, though slightly verbose. Could be trimmed slightly without losing information.

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 complexity (multiple data sources, fallbacks, date parsing), the description covers all necessary context. No output schema, but description mentions return shape. References sibling tool for alternative use case.

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% with descriptions for all three parameters. Description adds value by explaining the 'since' parameter supports relative shorthand ('7d', '30d', '3m', '1y') and that 'value' accepts ticker or CIK. Enhances beyond 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 states it provides a 'change feed for a company in the last N days/weeks/months' and differentiates from sibling tool 'entity_profile' by stating when to use the latter 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?

The description includes example queries, explicitly states when to use ('what's new with X', 'latest on Y') and when not ('Use entity_profile instead when you want the static profile'). Also explains fallback behavior (GDELT→GNews) and soft-fails.

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

The set mixes two unrelated domains (Guild Wars 2 endpoints and a broad Pipeworx data-research suite), and within each there are near-duplicates: ask_pipeworx vs ask_pipeworx_beta are functionally identical, commerce_prices vs guild_wars_2_item_price vs commerce_listings overlap on Trading Post data, and ask_pipeworx/ask_pipeworx_grounded/deep_research all route questions to the same source catalog. An agent could easily select the wrong tool.

Naming Consistency3/5

Names are all snake_case and readable, but there is no consistent pattern: some are bare nouns (achievements, currencies, quaggans, worlds, build), some are verb_noun (resolve_entity, validate_claim, generate_llms_txt), some are ask_* (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), and some are compound names (polymarket_kalshi_spread, guild_wars_2_item_price). Minor deviations would be fine, but this is a genuine mix of conventions.

Tool Count2/5

42 tools is far beyond the well-scoped range, and the majority are unrelated to the 'Guild Wars 2' server name (only ~11 tools are GW2 API endpoints; the rest are Pipeworx data-research/meta tools). This feels like two or three servers merged into one.

Completeness2/5

For a Guild Wars 2 server, the coverage is thin: it has items, prices, achievements, worlds, and WvW, but no recipes, guilds, characters, skills, maps, or PvE content. For the broader data-research domain implied by most tools, the surface is sprawling but still lacks depth in several areas. The result is a set that is neither complete for GW2 nor coherently scoped for anything else.