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Crawlora MCP

datasets_starbucks_stores_search

Read-only

Search the worldwide Starbucks store directory dataset (grid-tiled around the store locator's 50-result cap).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoOptional full-text search over store name, city, and address, max 256 characters.
latNoOptional latitude for radius filtering or distance sort, from -90 through 90; supply together with lon.
lonNoOptional longitude for radius filtering or distance sort, from -180 through 180; supply together with lat.
cityNoOptional exact city filter.
pageNoResult page number, 1-based, default 1; page times page_size must not exceed 10000.
sortNoOptional sort order. Allowed values: relevance, distance_asc. Defaults to store number order.
stateNoOptional state/region code filter, e.g. WA.
marketNoOptional crawl-provenance market filter, one of us or ca (the Starbucks host the store was discovered through, not its geography).
amenityNoOptional amenity code filter, e.g. DT for Drive-Thru or XO for Mobile Order and Pay.
countryNoOptional ISO-3166-1 alpha-2 country filter, e.g. US, GB, JP.
radius_mNoOptional radius in meters, from 1 through 50000; requires lat and lon.
page_sizeNoPage size, default 20, max 100; page times page_size must not exceed 10000.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesThe tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds one genuine behavioral trait beyond them: results are capped around 50 and the data is grid-tiled, which affects how an agent must query. It does not explain pagination interaction, throttling, or how the tiling interacts with the schema's page x page_size <= 10000 rule.

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?

A single efficient sentence with the primary purpose front-loaded and only one parenthetical of additional context. Nothing is padded, though the parenthetical is a bit compressed and could be clearer.

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 tool has 12 optional parameters and an output schema (so return values need no explanation), and the schema fully documents inputs. Still, for a dataset-search tool with nearby/item/facets siblings, the description omits the routing context an agent needs and leaves the 50-result/tiling mechanic underexplained.

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 all 12 parameters (q, lat, lon, city, page, sort, etc.) are already documented with constraints and enums in the schema itself. The description adds no additional parameter meaning, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb (Search) and resource (worldwide Starbucks store directory dataset), so an agent knows exactly what it retrieves. However, it does not distinguish this tool from its close siblings datasets_starbucks_stores_nearby, datasets_starbucks_stores_item, or datasets_starbucks_stores_facets, all of which operate on the same dataset.

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

Usage Guidelines3/5

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

The parenthetical about being 'grid-tiled around the store locator's 50-result cap' implies a usage constraint (split your queries spatially to get full coverage), but it is left implicit. There is no explicit when-to-use guidance and no reference to the nearby/item/facets siblings that would help the agent route correctly.

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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