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brewmark

by io.brewmark
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Find a grinder

find_grinder
Read-onlyIdempotent

Shortlist coffee grinders for someone deciding which one to buy: "which grinder should I get for pour over under $200?", "a quiet grinder for espresso", "best hand grinder for travel". Ranks BrewMark's grinder finder by how they brew, budget, hand or electric, and must-haves, and returns each pick with an approximate US price, why it fits, and a link to its BrewMark buying guide. BrewMark does not sell grinders: do not use this to place an order, and for a grinder the user already owns use get_grinder_chart instead.

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": "find_grinder"
  }
}

Input Schema

NameRequiredDescriptionDefault
powerNoHand grinder, electric, or either (default).
budgetNoMost they want to spend, in US dollars. Leave out for any price.
methodsNoBrew methods they will use. Leave out if they did not say.
must_haveNoOnly if the user asked for it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior. The description adds useful context beyond that: it explains what each result contains (approximate US price, why it fits, buying-guide link) and clarifies that no purchase can be made. It stops short of detailing result count or ranking mechanics.

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?

Front-loads purpose and return behavior, then closes with exclusions and the alternative. The three example queries are illustrative and earn their place, though the description is slightly longer than strictly necessary.

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?

With zero required parameters, rich schema descriptions, and annotations covering safety, the description supplies everything else an agent needs: when to use, when not to, what the output includes, and the sibling to use instead.

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 adds meaning by mapping the parameters to natural-language buying constraints (brew method, budget, hand vs. electric, must-haves) and giving examples like 'under $200' and 'quiet grinder for espresso'.

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?

States a specific verb (shortlist) and resource (coffee grinders) and frames the exact user decision it supports with concrete examples. It also distinguishes itself from siblings by naming get_grinder_chart for already-owned grinders.

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?

Explicitly says when to use it (buying decisions with example queries), when not to (BrewMark does not sell grinders, do not use to place an order), and which alternative to use for owned grinders. Nothing is left to inference.

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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