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yelp_search: GET /

hasdata_yelp_search_getSearchResults
Read-only

Get Yelp Search Results

Runs a Yelp business search by keyword and location with optional map-bounded radius via the l parameter (g:lon1,lat1,lon2,lat2), domain targeting, and offset-based pagination. Returns a ranked list of businesses with Yelp alias/ID, name, categories, rating, review count, price tier, and thumbnail. Use the returned aliases as input to the Yelp Place endpoint for full details, to power local-discovery UIs, or to build market-share/competitor datasets for a niche in a given geography.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lNoParameter defines the distance or map radius for the search results. For example: `g:-95.2486,29.8496,-95.4277,29.6324`.
startNoResult offset for pagination (e.g., 0 for the first page, 10 for the 2nd page, etc.).
domainNoYelp domain to use. Default is `www.yelp.com`.
keywordYesThe search term for which to get the search results.
locationYesThe location where to search for businesses with the given keyword.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/openWorldHint/destructiveHint, so the safety profile is covered. The description adds behavior beyond that: it enumerates the returned fields (alias/ID, name, categories, rating, review count, price tier, thumbnail) and notes pagination is offset-based, which matters since there is no output schema.

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?

Purpose and scope are front-loaded in the first sentence, with the return shape and downstream uses following. The final clause stacks three use cases, which is slightly listy, but nothing is wasted or circular.

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 5-parameter read-only search tool with no output schema, the description covers the required inputs, the optional radius/domain/pagination behavior, and the return contents. It is complete enough to invoke correctly; only auth/rate-limit context is absent, which is minor for this tool class.

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 coverage is already 100%, so the baseline is 3, but the description genuinely clarifies the `l` parameter's meaning ("map-bounded radius ... g:lon1,lat1,lon2,lat2"), which the schema only demonstrates by bare example, and labels `start` as offset-based pagination. It adds real meaning over the structured fields.

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 names a specific verb and resource ("Runs a Yelp business search by keyword and location") and scopes it further with the optional map-bounded radius, domain targeting, and offset pagination. It also differentiates itself from the sibling place endpoint by saying returned aliases feed into it, so an agent can tell the search tool apart from the detail 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?

It gives concrete usage context: feed returned aliases to the Yelp Place endpoint for details, or use results for local-discovery UIs and competitor/market-share datasets. It stops short of explicit exclusions (e.g., when to prefer the reviews endpoint instead), but the when-to-use context is clear.

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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