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Metmuseum

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").

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate the tool is read-only and idempotent. The description adds behavioral details such as fanning out to multiple sources, fallback logic, and soft-failure for USPTO.

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 well-structured and front-loaded with examples. While it is packed with information, every sentence adds value and there is no redundancy.

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?

The description covers multiple data sources, return fields, and fallback. Lacks details on pagination or error handling but is sufficient for a complex 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?

With 100% schema coverage, baseline is 3. The description adds meaning by explaining 'since' formats and recommending '30d' or '1m' for monitoring.

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 specific company over a time window, fanning out to multiple data sources. It includes concrete usage examples and distinguishes itself from the sibling tool entity_profile.

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 explicitly tells when to use this tool versus entity_profile (static profile) and provides example queries. It 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

A3.5/5.0
Disambiguation2/5

The server mixes two Met-specific tools (get_artwork, search_artworks) with a large set of generic Pipeworx tools (ask_pipeworx, bet_research, etc.), making it unclear which tools actually relate to the Met museum. Agents will struggle to distinguish the domain-specific tools from the general data tools.

Naming Consistency2/5

Tool names follow no consistent pattern: Met-specific tools use get_/search_/list_ prefixes, while Pipeworx tools use diverse patterns (ask_, bet_, compare_, discover_) and some use underscores while others lack verbs. The inconsistency increases cognitive load.

Tool Count3/5

At 29 tools, the count is high but not unreasonable for a combined server. However, only 3 tools are Met-specific, so the count feels inflated by unrelated tools. A more focused Met server would have fewer tools.

Completeness2/5

For a Met museum server, the tool surface is severely limited: only search, get by ID, and list departments. Missing operations like filtering by artist, retrieving related objects, or accessing collection highlights. The domain coverage is incomplete.

Resources