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Crawlora MCP

starbucks_stores

Read-only

Find nearby Starbucks stores worldwide: store number, name, phone, full address, coordinates, weekly opening hours, amenities, and pick-up options. Either place, or both lat and lng, is required. place is free-text (city, address, or postal code) geocoded by Starbucks itself, so it works worldwide. market selects which Starbucks country site answers, one of us or ca (default us); this matters even for stores, because the same store reports different operational data depending on the host. There is no filter parameter: Starbucks' own API accepts a features amenity filter but silently ignores it, so it is deliberately not offered here; filter on each store's returned amenities instead. A place that cannot be resolved returns a well-formed empty result with place_not_found true rather than an error. At most 50 stores are returned per request and there is no pagination; result_capped marks that ceiling. Store discovery works worldwide, but hours, amenities, and phone numbers are populated per market and may be absent outside the US and UK.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "starbucks_stores"
  }
}

Input Schema

NameRequiredDescriptionDefault
latNoLatitude, requires lng
lngNoLongitude, requires lat
placeNoFree-text city, address, or postal code, geocoded by Starbucks
marketNoStarbucks country site to read. One of: us, ca. Defaults to us. Documented values: us, ca.

Output 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. Changed4 schema fields changed
    • addedInput schema / properties / lat / description
      Added value: +"Latitude, requires lng"
    • addedInput schema / properties / lng / description
      Added value: +"Longitude, requires lat"
    • addedInput schema / properties / market / description
      Added value: +"Starbucks country site to read. One of: us, ca. Defaults to us. Documented values: us, ca."
    • addedInput schema / properties / place / description
      Added value: +"Free-text city, address, or postal code, geocoded by Starbucks"
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Goes well beyond the readOnlyHint/openWorldHint annotations: documents the 50-store cap and lack of pagination with the result_capped marker, the well-formed empty result with place_not_found true instead of an error, and the market-dependent availability of hours/amenities/phone. These are exactly the operational behaviors an agent cannot infer from annotations.

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?

Long but information-dense: capability is front-loaded, then input rules, then market semantics, then the deliberately-absent filter, then the error/cap behavior, then data-completeness caveats. No sentence is filler or redundant with annotations or schema.

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?

With an output schema present the description need not explain return values, yet it still covers the response edge cases (result_capped, place_not_found) and the geographic limits on hours/amenities/phone. Nothing an agent needs to call this correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline would be 3, but the description adds genuine meaning: place is geocoded by Starbucks itself (hence worldwide), and market changes which host answers and therefore which operational data a store reports. That is real semantic value beyond the schema text.

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?

States a specific verb and resource ('Find nearby Starbucks stores worldwide') and enumerates the returned fields, so the agent knows exactly what this does. It does not name or contrast with the closely related sibling starbucks_nearest_store (or the datasets_starbucks_stores_* family), so sibling differentiation is left implicit rather than explicit.

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 states the input requirement (either place, or both lat and lng), explains when market matters and why, and proactively rules out a filter parameter with the alternative behavior to use instead ('filter on each store's returned amenities'). This is when-to-use, when-not-to-use, and alternatives all in one.

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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