Crawlora MCP

starbucks_nearest_store

Read-only

Locate the single closest Starbucks store to a coordinate. Both lat and lng are required. Returns the nearest store's coordinates and distance only, not its details -- this is what Starbucks' own locator uses to centre its map. Use starbucks_stores for full store records. market selects which Starbucks country site answers, one of us or ca (default us). A point with no nearby store returns a well-formed result with found false rather than an error.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "lat": "<lat>",
      "lng": "<lng>"
    },
    "name": "starbucks_nearest_store"
  }
}

Input Schema

Name	Required	Description
`lat`	Yes	Latitude
`lng`	Yes	Longitude
`market`	No	Starbucks country site to read. One of: us, ca. Defaults to us. Documented values: us, ca.

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.

Changed3 schema fields changedOct 7, 2026
- addedInput schema / properties / lat / description
```
Added value: +"Latitude"
```
- addedInput schema / properties / lng / description
```
Added value: +"Longitude"
```
- addedInput schema / properties / market / description
```
Added value: +"Starbucks country site to read. One of: us, ca. Defaults to us. Documented values: us, ca."
```
First observedOct 7, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations only declare readOnlyHint and openWorldHint. The description goes further: it discloses the return shape (coordinates and distance only, not details), the rationale (mirrors Starbucks' own locator), and the no-result behavior (well-formed result with found false rather than an error). This is exactly the behavioral context annotations cannot carry.

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 is front-loaded, then constraints, alternative, market semantics, and the edge case follow in a logical order. It is dense but every clause contributes; a slight trimming of the market/required restatement would tighten it further.

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 needn't explain return values, and it still covers the no-match case and the sibling routing. 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.

Parameters3/5

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

Schema coverage is 100% and an output schema exists, so the schema already documents lat, lng, and market. The description's market explanation ('selects which Starbucks country site answers') and required lat/lng note largely restate the schema, adding only marginal semantic value. Baseline 3 is appropriate when the schema does the heavy lifting.

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 (Locate) and resource (the single closest Starbucks store) scoped to a coordinate, and explicitly distinguishes itself from the sibling starbucks_stores by naming it. An agent can pick this apart from other Starbucks tools without opening any 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?

Gives explicit when-to-use (find the nearest store to a point) and names the alternative (starbucks_stores) with the condition that selects it (full store records). Nothing is left 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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