Crawlora MCP

datasets_starbucks_stores_search

Read-only

Search the worldwide stored Starbucks store directory, assembled by grid-tiling around the locator's 50-result cap. Costs 1 credit per record returned (minimum 2), so keep page_size to what you need. Use datasets_starbucks_stores_nearby for live nearby results, datasets_starbucks_stores_item for one store, or datasets_starbucks_stores_facets to inspect filters.

Input Schema

Name	Required	Description
`q`	No	Optional full-text search over store name, city, and address, max 256 characters.
`lat`	No	Optional latitude for radius filtering or distance sort, from -90 through 90; supply together with lon.
`lon`	No	Optional longitude for radius filtering or distance sort, from -180 through 180; supply together with lat.
`city`	No	Optional exact city filter.
`page`	No	Result page number, 1-based, default 1; page times page_size must not exceed 10000.
`sort`	No	Optional sort order. Allowed values: relevance, distance_asc. Defaults to store number order.
`state`	No	Optional state/region code filter, e.g. WA.
`market`	No	Optional crawl-provenance market filter, one of us or ca (the Starbucks host the store was discovered through, not its geography).
`amenity`	No	Optional amenity code filter, e.g. DT for Drive-Thru or XO for Mobile Order and Pay.
`country`	No	Optional ISO-3166-1 alpha-2 country filter, e.g. US, GB, JP.
`radius_m`	No	Optional radius in meters, from 1 through 50000; requires lat and lon.
`page_size`	No	Page size, default 20, max 100; page times page_size must not exceed 10000.

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 7, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations only say readOnly and openWorld; the description adds a real cost model (1 credit per record, minimum 2) plus a data-completeness caveat about grid-tiling. This is exactly the kind of rate/cost behavior structured fields do not convey.

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?

Three sentences, front-loaded with purpose then cost warning then alternatives, with zero redundancy. Every clause carries distinct decision-relevant information.

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

Completeness5/5

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

An output schema exists, so return values need no explanation. Cost, data-coverage caveat, and sibling routing are all covered, leaving nothing an agent needs to call this correctly.

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 12 parameters, establishing a baseline of 3. The description only echoes one parameter (page_size) via the cost tip and adds no syntax or format detail beyond the schema.

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?

States a specific verb and resource ('Search the worldwide stored Starbucks store directory') and adds a provenance detail (grid-tiled assembly around the locator's 50-result cap) that no sibling repeats. An agent can distinguish it from the nearby/item/facets tools without opening a schema.

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

Usage Guidelines5/5

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

Explicitly routes the agent: nearby for live nearby results, item for one store, facets to inspect filters. It names the alternatives and the condition selecting each, leaving nothing to inference.

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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