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Crawlora MCP

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steam_community_recommendations

Get recent quality user reviews recommended across the Steam store, with filters for language, playtime, and review kind.

Instructions

Get the store's community-recommended reviews feed. Returns a batch of recent, quality user reviews recommended across the whole store (author, playtime, helpful votes, and the recommended app). Filter by review kind/sort, reviewer playtime window, review language, and store region. The upstream serves a fixed batch and, unauthenticated, does not support tag filtering or deep pagination. Credential-free public Steam storefront JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lNoLanguage code
ccNoStore country code (ISO, selects currency)
playtime_maxNoMaximum reviewer playtime in hours (0 = no maximum)
playtime_minNoMinimum reviewer playtime in hours (0 = no minimum)
review_filterNoReview kind / sort
review_languageNoReview language: 'my_languages' or a Steam language name
Behavior3/5

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

No annotations provided. Description discloses that the upstream serves a fixed batch, is credential-free, and does not support tag filtering or deep pagination. Lacks info on rate limits, error handling, or data freshness.

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?

Single paragraph, 4 sentences, each adding value. Front-loaded with main purpose. No wasted words.

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?

Describes output structure (author, playtime, etc.) and limitations. No output schema, so description compensates well. Missing explicit batch size or pagination details, but sufficient for a feed tool.

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% so the baseline is 3. The description adds context by grouping filters (e.g., 'review kind/sort') but does not significantly extend the schema's own descriptions.

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 clearly states it fetches community-recommended reviews across the whole store, listing returned fields (author, playtime, etc.). This distinguishes it from siblings like steam_reviews which are per-app.

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?

Explicitly lists filter dimensions (review kind, playtime window, language, region) and notes limitations: no deep pagination, no tag filtering without auth. Context is clear but no explicit 'when to use vs alternatives' statement.

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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