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Crawlora MCP

by net.crawlora
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steam_items

Read-only

Resolve up to 100 Steam app IDs to store items with weighted community tags. Use steam_app for one app's full store details instead of batching IDs.

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": {
      "appids": "<appids>"
    },
    "name": "steam_items"
  }
}

Input Schema

NameRequiredDescriptionDefault
lNoSteam store language name
ccNoStore country code (ISO, selects currency)
appidsYesComma-separated numeric app ids (max 100)

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. Changed3 schema fields changed
    • addedInput schema / properties / appids / description
      Added value: +"Comma-separated numeric app ids (max 100)"
    • addedInput schema / properties / cc / description
      Added value: +"Store country code (ISO, selects currency)"
    • addedInput schema / properties / l / description
      Added value: +"Steam store language name"
  2. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds that returned items carry 'weighted community tags', which hints at result content, but says nothing about batching behavior, rate limits, or error handling. With annotations and an output schema present, this is adequate but not rich.

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?

Two tight sentences, front-loaded with the core purpose and followed by the disambiguation. No filler; every clause carries information.

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 full schema coverage, an output schema, and read-only/open-world annotations already supplied, the description covers the remaining gaps (purpose, sibling routing, batch limit). Nothing essential for correct invocation 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 description coverage is 100%, so the schema already documents l, cc, and appids. The description's only parameter-related content ('up to 100 app IDs') restates the schema's max-100 note, adding no new syntax or format meaning. Baseline 3 is correct.

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 ('Resolve') and resource ('Steam app IDs to store items with weighted community tags'), and explicitly distinguishes itself from the steam_app sibling. An agent can identify the tool's function without opening the schema.

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 routes the agent: 'Use steam_app for one app's full store details instead of batching IDs' names the alternative and the selecting condition (single app vs batch). It lacks an explicit when-not (e.g., that batching is pointless for a single ID, or the 100 limit as a usage constraint), but the guidance is clear.

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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