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cfpramod

open-museum-mcp

discover_random

Pick a random artwork from cache that matches specified constraints (region, period, artist exclusion, museum). Auto-seeds cache for first use. Use to break repetitive search patterns and discover unexpected works.

Instructions

Pick one random artwork from the local cache that matches the given constraints. Useful for breaking out of repetitive search territory (e.g. surface a random Edo-period work to satisfy a no-back-to-back-European-pre-1900 pairing rule). On a cold cache with region or period constraints provided, auto-seeds via a small search_artworks call derived from those constraints before sampling — so first-time use works without manually warming the cache. Without constraints, returns a hint to run search_artworks first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionNoNormalized region tag (e.g. "china", "japan", "netherlands"). Exact match.
periodNoNormalized period tag (e.g. "tang dynasty", "edo", "safavid"). Exact match.
not_artistNoArtist names to exclude (exact match against the canonical artist name field).
museumNoOptional museum code to restrict to (met, cleveland, aic).
Behavior5/5

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

No annotations exist, so description fully discloses caching behavior, auto-seeding on cold cache with region/period, and hint when no constraints. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise yet comprehensive, front-loaded with purpose followed by usage scenario and technical details. Every sentence contributes meaningfully.

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?

No output schema exists; description implies return of an artwork but doesn't detail format. However, it thoroughly covers parameters, caching, and constraints, making it sufficiently complete for a random-selection 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%, so baseline is 3. Description adds value by explaining how parameters (region, period) trigger auto-seeding and notes exact matching for not_artist, going 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 explicitly states the tool picks a random artwork from a local cache matching constraints, differentiating it from search_artworks and other siblings.

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?

Provides clear when-to-use guidance (breaking repetitive search), includes a concrete example scenario, and explains behavior with and without constraints, suggesting alternatives.

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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