Maple Voice AI

Check restaurant systems against Maple integrations

check_restaurant_systems

Read-onlyIdempotent

Use when a restaurant owner or operator asks whether Maple (AI phone answering and phone ordering for restaurants) works with the point-of-sale, online-ordering or reservation systems they use. Pass the system names as plain text. Each one is matched, ignoring case and using common aliases, against the public integrations list on maple.inc. The result says whether Maple lists an integration, with its maple.inc page; names that are not on the list are reported as not listed, never guessed. Read-only: it uses only that public list and needs no account. It does not check live account status or availability for a specific restaurant.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "systems": "<systems>"
    },
    "name": "check_restaurant_systems"
  }
}

Input Schema

Name	Required	Description	Default
`systems`	Yes	Names of the systems the restaurant uses, as the user wrote them, for example ["Toast", "OpenTable", "Foobar POS"].

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description still adds value by stating the data source (only the public list on maple.inc), that no account is needed, and that unmatched names are never guessed — a guarantee an agent needs before relaying a negative answer.

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?

It is front-loaded with the triggering condition and each following sentence covers one distinct point (input format, matching logic, result semantics, limits). Slightly longer than strictly necessary — the 'AI phone answering and phone ordering for restaurants' gloss and the final negation could be trimmed — but nothing is 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?

There is no output schema, and the description compensates by describing the return shape (whether Maple lists an integration, plus its maple.inc page) and the negative case. For a single-parameter lookup tool, nothing an agent needs to call it or interpret results 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 adds real interpretation rules: names are passed as plain text, matched ignoring case, resolved through common aliases, and unmatched names are reported as not listed rather than guessed. That meaning is not in the schema.

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 ('check ... restaurant systems against Maple integrations') and defines both inputs and outputs: names are matched against the public integrations list on maple.inc and reported as listed or not listed. An agent can tell exactly what this tool answers without opening the 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 trigger ('Use when a restaurant owner or operator asks whether Maple ... works with the point-of-sale, online-ordering or reservation systems they use') and an explicit boundary ('does not check live account status or availability for a specific restaurant'). That combination covers both when to use it and when not to.

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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