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Crawlora MCP

Crawlora MCP

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by Crawlora-org
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JavaScript
Remote

datasets_starbucks_stores_item

Get a specific Starbucks store's details by store number from the starbucks-stores dataset, including country, market, schedule, and amenities for location lookup.

Instructions

Get a stored Starbucks store. Returns one stored Starbucks store by its store number (e.g. 101-54) from dataset id starbucks-stores. country is the store's true country while market is crawl provenance. Hours (schedule) and amenities may be empty for stores outside the US, Canada, Europe, and the Gulf; an empty schedule means "not published for this market", not "closed".

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": {
      "store_number": "<store_number>"
    },
    "name": "datasets_starbucks_stores_item"
  }
}

Input Schema

NameRequiredDescriptionDefault
store_numberYesStarbucks store number, e.g. 101-54

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and succeeds impressively. It discloses field provenance semantics (country is true country, market is crawl provenance) and provides a critical data-quality caveat: empty schedule means 'not published for this market', not 'closed'. This prevents an agent from making a false business inference (e.g., reporting a store as closed) and is exactly the kind of value-added context the dimension seeks.

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?

Four sentences, front-loaded with purpose, followed by two targeted behavioral notes. A minor redundancy exists between 'Get a stored Starbucks store' and 'Returns one stored Starbucks store', but every sentence otherwise earns its place and the caveats are essential rather than padding.

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 single-parameter lookup with no annotations and no output schema, the description covers the critical ground: what it returns, how to identify the record, and how to interpret ambiguous fields. The main gap is that the return structure is not described and no output schema compensates, but for a simple 'get by id' pattern the interpretation guidance outweighs this omission.

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%: the only parameter, store_number, is already fully documented in the schema with the same example (101-54) the description repeats. The description adds no meaning beyond the schema — it reinforces the format but contributes nothing new. Baseline 3 for full schema coverage is appropriate.

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 states a specific verb ('Get') and resource ('stored Starbucks store'), then adds precision: returns ONE store by store number from a named dataset. This distinguishes it from sibling tools like datasets_starbucks_stores_search (many stores) and datasets_starbucks_stores_nearby (location-based lookup) without needing to inspect them.

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?

The lookup-by-store-number pattern is unambiguous: when an agent has a specific store number and wants the stored record for it, this is the tool. The data provenance field notes ('country' vs 'market', schedule semantics) additionally guide interpretation of what comes back. However, it never explicitly names alternatives like datasets_starbucks_stores_search or states when NOT to use it, so it falls short of a 5.

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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