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Rate a recipe

rate_recipe
DestructiveIdempotent

Record the user's stars for a recipe, 1 to 5, for "five stars", "that one was a 3", "best curry we've had", with an optional note on why. One rating per user and recipe; rating again replaces it. The stars join the recipe's rating: the rating and rating_count every tool returns average the source site's ratings and ask.recipes users' stars together, and the user's own stars come back as my_rating. When the user also says they cooked it, mark_cooked with rating does both in one call.

Input Schema

NameRequiredDescriptionDefault
idYesrecipe id to rate
noteNowhy, in the user's words, e.g. 'too sweet, halve the sugar'; at most 200 characters; empty keeps the note from the earlier rating
starsYes1 to 5 stars; replaces the user's earlier rating of this recipe

Output Schema

NameRequiredDescriptionDefault
recipeYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the annotations, it discloses the upsert semantics (one rating per user and recipe, rating again replaces it), how the user's stars feed the aggregate rating and rating_count returned by every tool, and that the user's own stars surface as my_rating. This makes the destructive/replacement behavior concrete rather than leaving the agent to infer it from destructiveHint.

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 intent and trigger phrases are front-loaded and every sentence carries information, but the middle sentence covering aggregation, rating_count, and my_rating is dense and slightly overlong. It is efficient overall, with no filler.

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?

For a three-parameter annotation-covered mutation with an output schema, the description covers the trigger, the write semantics, the replacement behavior, and the cross-tool delegation to mark_cooked. Nothing an agent needs in order to call it correctly is missing.

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 the baseline is 3, but the description still adds value by mapping colloquial utterances to the stars parameter and stating the allowed range inline. It also frames the note as an optional "why", giving the parameter a purpose beyond the schema's field description.

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 names a specific verb and resource (record stars for a recipe), gives the natural-language phrasings that should trigger it ("five stars", "that one was a 3", "best curry we've had"), and distinguishes it from the sibling mark_cooked. An agent can identify the intent without opening any schema.

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?

It gives an explicit routing rule: when the user also says they cooked it, use mark_cooked with rating to do both in one call. That names the alternative and the condition that selects it, which is exactly the guidance an agent needs to choose between the two rating paths.

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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