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tabelog-mcp

tabelog-mcp

by ingrid-wu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Booking preferences

get_preferences
Read-only

Retrieve saved and learned dining preferences—seating windows, party size, budget, private rooms, areas, and repeat restaurants—to tailor personal or business restaurant searches and bookings.

Instructions

Saved preferences (what the user said: seating windows, party, private room, walking limit, budget, notes) and learned ones (from confirmed bookings: usual genres, areas, party, time, repeat restaurants).

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": "get_preferences"
  }
}

Input Schema

NameRequiredDescriptionDefault
purposeNobusiness (default) or personal.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already establish that this is a safe read-only, non-destructive, closed-world operation. The description adds valuable transparency about provenance: saved preferences come from what the user said, while learned preferences are inferred from confirmed bookings, which helps an agent interpret reliability of the data.

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?

The description is a single dense sentence that front-loads the core distinction between saved and learned preferences. The parenthetical field lists are efficient, though they make the sentence slightly harder to scan at a glance.

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

Completeness4/5

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

For a simple read-only tool with no output schema, the description gives a solid inventory of returned data and distinguishes saved from learned sources. The only meaningful gap is the unmentioned 'purpose' filter and the absence of usage guidance, but the core return content is adequately covered.

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%, and the single optional 'purpose' parameter is fully documented in the schema, including its business default. The description does not mention this parameter or add any semantics beyond the schema, so it meets but does not exceed the baseline for high 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 resource as saved and learned booking preferences, and enumerates the kinds of data returned (seating, party size, budget, genres, areas, etc.). It does not explicitly contrast with the sibling set_preferences, but the read-oriented 'saved/learned preferences' framing makes the purpose distinguishable.

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?

The description explains what the tool returns but gives no guidance on when to call it versus set_preferences or other booking tools. There are no conditions, prerequisites, or exclusions to help an agent choose this tool over its siblings.

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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