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Restaurant Booking MCP Server

Restaurant Booking MCP Server

by samwang0723
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

make_reservation

Book a restaurant reservation by providing details like party size, preferred date and time, contact information, and special requests using the Google Places ID of the restaurant.

Instructions

Attempt to make a restaurant reservation (mock implementation)

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": {
      "placeId": "<placeId>",
      "partySize": "<partySize>",
      "preferredDateTime": "<preferredDateTime>",
      "contactName": "<contactName>",
      "contactPhone": "<contactPhone>"
    },
    "name": "make_reservation"
  }
}

Input Schema

NameRequiredDescriptionDefault
contactEmailNoEmail address (optional)
contactNameYesName for the reservation
contactPhoneYesPhone number for the reservation
localeNoLocale for reservation process (e.g., "en", "zh-TW", "ja", "ko")en
partySizeYesNumber of people in the party
placeIdYesGoogle Places ID of the restaurant
preferredDateTimeYesPreferred date and time in ISO format
specialRequestsNoAny special requests or dietary restrictions

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a 'mock implementation', which hints at limited functionality, but doesn't describe what that means operationally (e.g., whether it actually creates reservations, returns simulated results, or has specific limitations). For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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?

The description is a single, efficient sentence that communicates the core purpose without any wasted words. The parenthetical '(mock implementation)' is appropriately placed and adds necessary context without disrupting flow.

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

Completeness2/5

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

For a mutation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what happens after the 'attempt' (success/failure outcomes, return format, error conditions), nor does it address behavioral aspects like authentication needs or rate limits that would be crucial for an agent to use it effectively.

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 the schema already documents all 8 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter semantics.

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 states the tool's purpose with a specific verb ('make') and resource ('restaurant reservation'), and the parenthetical '(mock implementation)' adds useful context about its nature. However, it doesn't explicitly distinguish this tool from its siblings like 'check_availability' or 'get_booking_instructions', which would be needed for a perfect score.

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 provides no guidance on when to use this tool versus alternatives like 'check_availability' or 'get_booking_instructions'. It doesn't mention prerequisites (e.g., whether availability should be checked first) or appropriate contexts, leaving the agent to guess based on tool names alone.

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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