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Metmuseum

by io.github.pipeworx-io

What Can I Ask Pipeworx?

suggest_questions
Read-onlyIdempotent

What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass topic (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoOptional focus area: finance | pharma | economics | real-estate | betting | weather | government | science | news. Omit for a cross-category spread.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. Description adds that it returns 'category-bucketed example questions' from a live catalog, and explains how the topic parameter affects scope—valuable context beyond 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-organized with a list of common queries, clear explanation of output, and usage instructions. Slightly verbose but every sentence contributes. Could be tightened without losing substance.

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 a single optional parameter and no output schema, the description fully covers the tool's purpose, behavior, and usage context. It even situates the tool within a broader workflow (first use, learning meta-tools). Perfectly complete.

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 by listing example topic values (finance, pharma, etc.) and explaining the effect of omission (cross-category spread). This is above the baseline of 3.

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?

Clearly states the tool is an onboarding entry point that returns category-bucketed example questions, with explicit mention of verb ('get started', 'learn'), resource ('Pipeworx capabilities'), and sufficient distinction from siblings by positioning it as a first-step discovery tool.

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 instructs to use this tool first when unfamiliar with Pipeworx, and to learn how to call meta-tools. Provides optional topic parameter guidance and describes behavior with no arguments.

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.

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