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Crawlora-org

Crawlora MCP

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opentable_search

Search for restaurants on OpenTable near a location using free-text terms, and get live availability for your party size and time. No login required.

Instructions

Search OpenTable restaurants near a location. Searches restaurants by free-text term (cuisine, name, neighborhood) near a latitude/longitude, for a given date/time and party size, including inline live availability per result. Credential-free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoMax results, default 10
termYesFree-text search term
latitudeYesSearch center latitude
date_timeNoReservation date/time, RFC3339-minute local format; defaults to now
longitudeYesSearch center longitude
party_sizeNoParty size, default 2
Behavior4/5

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

With no annotations, the description carries the burden. It discloses that results include inline live availability and that the tool is credential-free, adding useful behavior context. However, it omits details like pagination or result limits.

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, and every clause adds value. No redundancy or filler.

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?

Covers all key input dimensions (term, location, time, party size) and notes the live availability output. Without an output schema, the availability note helps predict returns, but deeper response structure and edge-case behaviors are not described.

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 covers all parameters, so baseline is 3. The description adds meaning by explaining 'term' as cuisine/name/neighborhood and clarifying that lat/long define the search center, plus date/time and party size context.

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 specifies a search over OpenTable restaurants with location, free-text term, date/time, and party size, and highlights inline live availability. This differentiates it from opentable_restaurant (likely detail lookup) and other domain search tools.

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?

It clearly describes when to use the tool: find restaurants matching a term near a location with availability. It does not explicitly name alternative tools or exclusions, but the context is clear enough for an agent to select it over a restaurant detail tool.

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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