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bguillow-rgb

percolate-mcp

What should I brew?

what_to_brew
Read-onlyIdempotent

Find a coffee for right now by matching your time of day, mood, and brew method against Percolate's tasting profiles to get a scored recommendation.

Instructions

A coffee suggestion for right now, based on time of day (evening picks lean decaf), mood, and the brew method you're using — scored over Percolate's tasting profiles.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
moodNoe.g. 'need focus', 'lazy weekend', 'something comforting', 'adventurous'
brew_methodNoWhat you're brewing with
time_of_dayNo
Install Server

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds useful conditional behavior ('evening picks lean decaf') and a scoring source, but it does not describe the return shape or any environment dependencies.

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 front-loaded sentence that communicates purpose and key influencing factors with no fluff. Even the contextual detail about evening decaf earns its place by signaling how time_of_day influences the result.

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

Completeness3/5

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

The tool is fairly simple and the annotations cover safety, but there is no output schema and the description does not state what a recommendation actually returns or what happens when parameters are omitted. It is adequate for basic invocation but leaves those details to be discovered.

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 coverage is 67%, and the description generally repeats relationship of mood, brew_method, and time_of_day without adding much detail. The main extra insight is the evening→decaf mapping; brew_method still lacks concrete possible values, so the description does not fully compensate for the medium schema coverage.

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 clearly identifies the tool as providing a coffee suggestion based on time of day, mood, and brew method — a specific verb+resource. However, it does not explicitly differentiate itself from siblings like `get_recommendations` or `dial_in_suggestion`, so it stays short of a 5.

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

Usage Guidelines3/5

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

The phrase 'for right now' and the listed inputs give a fairly clear context for when to use this tool: for an immediate, context-aware suggestion. There is no explicit mention of alternatives or when-not-to-use, so the guidance is present but not fully explicit.

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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