reservations-mcp

discover_restaurants

Find restaurants matching natural-language criteria like location, view, seating, and amenities. Filter by open now or outdoor seating, using data from Google Places and Yelp.

Instructions

Find restaurants by free-text criteria (e.g. 'Hudson River waterfront with Manhattan skyline view, outdoor seating, kid-friendly, great cocktails'). Read-only. Uses Google Places (richest: returns goodForChildren / outdoorSeating / servesCocktails) and/or Yelp depending on which API keys are configured.

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": {
      "query": "<query>"
    },
    "name": "discover_restaurants"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Max results per provider (default 20).
`query`	Yes	Natural-language search, include the city/area.
`openNow`	No
`latitude`	No
`location`	No	City/area text for Yelp, e.g. 'Weehawken, NJ'.
`longitude`	No
`outdoorSeating`	No	Yelp: require outdoor seating attribute.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 2, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It explicitly states 'Read-only,' a key behavioral trait, and discloses data source behavior (Google Places vs Yelp) and attribute richness (goodForChildren, outdoorSeating, servesCocktails). It does not mention rate limits or output details, but the read-only nature is well covered.

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?

Two sentences, front-loaded with the core purpose, includes a relevant example and backend context. Zero wasted words, ideal length and structure.

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?

The description covers the primary use case and notes the data sources, but with 7 parameters and no output schema, it lacks details on return format, pagination, or behavior when no API keys are configured. It is functional but has notable gaps for a tool of this complexity.

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 57% (4 of 7 params described). The description adds meaning by mentioning the query example and Google Places attributes that map to boolean filters (e.g., 'outdoor seating' relates to outdoorSeating parameter), but it does not clarify openNow, latitude, or longitude beyond the schema. It partially compensates but leaves gaps.

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 clearly states the tool 'finds restaurants by free-text criteria' with a concrete example, using a specific verb and resource. It distinguishes itself from sibling tools (resy_search, resy_booking, etc.) which are reservation/booking focused, making this the discovery tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for free-text restaurant discovery and notes it uses Google Places/Yelp depending on API keys, giving context. It does not explicitly name alternatives or exclusions, but the sibling tools are clearly different, so the usage context is clear enough.

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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