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talvola

bar-assistant-mcp

by talvola

bar_stats

Retrieve totals for cocktails, ingredients, and other bar metrics to track inventory and collection size.

Instructions

Get bar statistics (total cocktails, ingredients, etc).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations at all, the description carries the full behavioral burden. It implies a read-only aggregate lookup but says nothing about permissions, whether counts are cheap/cached, freshness of the data, or the shape of the response. It discloses only the general content type.

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 short sentence with the purpose front-loaded and zero filler. Nothing to trim.

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?

An output schema exists, so return values need not be explained, and a zero-param read tool has a low bar. Still, the vague 'etc' leaves the coverage of the statistics unspecified and there is no guidance on when this aggregate view is the right call versus the list tools.

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?

The tool takes zero parameters, so the schema requires no semantic elaboration; baseline is 4. The parenthetical examples add a little meaning about the statistics returned, though 'etc' remains vague.

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?

Names a specific verb ('Get') and resource ('bar statistics') and gives concrete examples of what is returned (total cocktails, ingredients). It is clearly distinct from the sibling list/search/get tools, though the sibling set itself makes it unambiguous rather than the description doing the work.

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?

No statement of when to use this versus alternatives, no prerequisites, no mention of what triggers a stats request. Usage is only implied by the word 'statistics'.

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