curator-mcp

Vet Recommendation Candidate

vet_recommendation_candidate

Vet a recommendation candidate by checking for library duplicates and calculating its taste affinity score.

Instructions

Check a recommendation candidate for library collision and calculate taste affinity score.

Input Schema

TableJSON Schema

Name	Required	Description
`domain`	Yes	The entertainment domain ('books', 'movies', 'whiskey', etc.).
`attributes`	No	Associated genres, flavor accords, or tags (optional).
`title_or_name`	Yes	Title of the book/movie or name of the artisanal item/venue.
`maker_or_creator`	No	Author, director, brand, or chef (optional).

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Sep 11, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states two operations (collision check and affinity scoring) but says nothing about side effects, permissions required, what happens on collision, or whether the operation is read-only.

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?

The description is a single sentence with no wasted words. The compound purpose is front-loaded and immediately scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with four parameters and no annotations, the description is too thin. While the output schema covers return values, the description lacks usage guidance, behavioral details, and any indication of how collision results or affinity scores should be interpreted.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all four parameters are documented in the input schema. The description adds no additional meaning about parameter usage, formats, or constraints beyond what the schema already provides, making the baseline score of 3 appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear compound purpose: check a recommendation candidate for library collision and calculate a taste affinity score. It identifies the resource (recommendation candidate) and the specific actions, but does not differentiate this tool from sibling tools like search_vault or get_user_taste_profile.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. The context of 'recommendation candidate' implies a vetting step, but the agent must infer this and receives no routing instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.