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Percolate

by io.github.bguillow-rgb
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what_to_brew

Read-onlyIdempotent

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.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "what_to_brew"
  }
}

Input Schema

NameRequiredDescriptionDefault
moodNoe.g. 'need focus', 'lazy weekend', 'something comforting', 'adventurous'
brew_methodNoWhat you're brewing with
time_of_dayNoWhen the coffee will be drunk — shifts the pick toward lighter or lower-caffeine options later in the day

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / brew_method / maxLength
      Added value: +120
    • addedInput schema / properties / mood / maxLength
      Added value: +120
  2. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already establish readOnly, idempotent, non-destructive and closed-world (openWorldHint=false). The description usefully adds that picks are scored over Percolate's tasting profiles and that evening selections lean decaf, which is genuine behavioral context. It does not say what the response actually contains (a single pick, a ranked list, scores), leaving the return shape opaque with no output schema to fall back on.

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?

A single dense sentence, front-loaded with the outcome ('a coffee suggestion for right now') and then the drivers. No filler, nothing repeated, and the most important clause comes first.

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?

For a zero-required-parameter read-only tool with full schema coverage and no output schema, the definition covers inputs adequately but leaves the return value unexplained — the agent cannot tell whether it gets one coffee, a ranked shortlist, or scores. Annotations carry the safety profile, but the output gap keeps this only minimally complete.

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 mood, brew_method and time_of_day are already well documented, including the enum semantics for time_of_day. The description restates the same mapping (time of day, mood, brew method, evening leaning decaf) without adding format, weighting, or default-behavior detail beyond the schema, so the baseline 3 applies.

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 names a specific action and resource: it returns a coffee suggestion to brew now, scored over Percolate's tasting profiles, driven by three named inputs. That is far more than a restatement of the name. It stops short of distinguishing itself from close siblings like get_recommendations or dial_in_suggestion, which an agent could easily confuse it with.

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?

'For right now' plus the time/mood/method inputs implies the decision moment this tool serves, so usage is reasonably inferable. However, it never states when to prefer this over get_recommendations or dial_in_suggestion, and gives no exclusions, so the agent must guess at routing.

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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