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yelp_place: GET /

hasdata_yelp_place_getPlaceDetails

Retrieve verified Yelp business details by ID or alias, including rating, review count, hours, amenities, and contact info, to enrich leads or validate venue information before displaying to users.

Instructions

Get Yelp Place Details

Fetches a single Yelp business by Yelp ID or alias with domain targeting. Returns name, address, phone, website, price range, categories, overall rating, review count, hours, amenities, photos, and highlighted reviews. Use to enrich leads or listings with verified Yelp metadata, monitor a competitor's rating and review count over time, or validate hours/amenities before displaying venue details to end users.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoYelp domain to use. Default is `www.yelp.com`.
placeIdYesThe Yelp ID or Yelp Alias of the place. For example, 'jPIZ3FR5LNcwPuUHi2Fe4g' or 'mcdonalds-new-york-386'.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the transparency burden. It clearly indicates a read-only fetch and lists the returned fields, but it does not mention authentication needs, rate limits, or error behavior. This is adequate but not deeply transparent.

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 well-structured: a brief fetch statement, a concise enumeration of returned fields, and practical use cases. The opening line is slightly redundant with the title, but overall every sentence contributes useful context.

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?

With no output schema, the description compensates by listing expected return fields. Required and optional parameters are covered by the schema, and use cases give an agent enough context to invoke the tool appropriately. Minor gaps around errors and data freshness remain.

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 both parameters are already documented with descriptions and an enum for domain. The description adds little semantic detail beyond what the schema provides, such as the notion of 'domain targeting' and the Yelp ID/alias examples.

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?

Description states a specific action ('Fetches a single Yelp business by Yelp ID or alias') and identifies the resource and returned data. It clearly distinguishes itself from the sibling review and search tools by emphasizing 'single business' details.

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 provides concrete use cases: enriching leads, monitoring competitor ratings over time, and validating hours/amenities. It does not explicitly contrast with sibling tools, but the context makes it clear when this details endpoint is appropriate.

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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