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Crawlora MCP

Crawlora MCP

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by Crawlora-org
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JavaScript
Remote

playstation_suggest

Fetch PlayStation Store search suggestions for a partial or full term, returning typeahead matches with game details, pricing, and store URLs for quick discovery.

Instructions

Get PlayStation Store search suggestions for a term. Returns typeahead-style PlayStation Store search suggestions for a partial or full term: id, name, classification, platforms, publisher, release date, price, a representative image, and a direct store URL per match. Matching is upstream-ordered and loose (it may include titles that only loosely relate to the term), not a ranked exact-substring search. limit caps how many suggestions are returned (max 20; the upstream API does not honor a higher value). cc selects the store region (and price currency); l accepts a language code or locale such as en or en-US, with the locale's primary language used for text.

Input Schema

NameRequiredDescriptionDefault
lNoLanguage code
ccNoStore country code (ISO, selects currency)
termYesSearch term (partial or full)
limitNoMaximum number of suggestions to return (max 20)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.5

TDQS

A4.5/5.0
Behavior4/5

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 does well: it discloses loose upstream-ordered matching, the returned fields, the max-20 limit behavior, and that the upstream API does not honor higher limits. It does not cover auth requirements, rate limits, or pagination, so it is strong but not exhaustive.

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?

The description front-loads the purpose and then progressively adds return fields, matching behavior, and parameter caveats. It is appropriately sized for the tool, though the first two sentences slightly restate the same core idea before adding useful detail.

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?

Given no output schema and no annotations, the description supplies the missing context: it lists the returned item fields, explains matching behavior, and clarifies the non-obvious parameter constraints. An agent has enough information to call and interpret this tool correctly.

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 description coverage is already 100%, so the baseline would be 3. The description adds meaningful detail beyond the schema: limit explains the upstream max-20 enforcement, cc explains region/currency selection, and l adds locale examples (en, en-US) and the primary-language rule for text.

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 starts with a specific verb and resource: 'Get PlayStation Store search suggestions for a term.' It also distinguishes this from an exact ranked search by saying matching is 'loose' and 'not a ranked exact-substring search,' which is enough to separate it from the sibling playstation_search.

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 description gives clear context for when this tool is appropriate: typeahead-style suggestions for a partial or full term, with loose upstream matching. It implicitly excludes exact-search use cases, but it does not explicitly name an alternative tool or state a full when-to-use/when-not rule.

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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