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datasets_steam_achievements_facets

Discover Steam achievement app IDs by returning up to 50 count-ordered app_id buckets, filtered by exact app_id using the same filter as the search dataset.

Instructions

Discover Steam achievement app ids. Returns up to 50 Steam achievement app-id buckets ordered by count, using the same app_id filter as datasets_steam_achievements_search. Page and page_size do not change the aggregation.

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": {
      "facet": "<facet>"
    },
    "name": "datasets_steam_achievements_facets"
  }
}

Input Schema

Name	Required	Description
`page`	No	Result page, 1-based
`sort`	No	Validated search sort; bucket order stays count-desc, and time-series date_desc keeps its search history scope
`facet`	Yes	Facet field
`app_id`	No	Exact Steam app id
`page_size`	No	Page size, default 20 and max 100

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.17.20Nov 12, 2026

TDQS

A3.8/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does disclose genuinely non-obvious behavior: a 50-bucket cap, count-descending ordering, and the important caveat that page/page_size do not change the aggregation. It omits anything about auth, rate limits, or what fields a bucket contains, but for a read-only facets endpoint those gaps are modest.

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?

Three short sentences, front-loaded with purpose and return shape, then the filtering tie-in, then the pagination caveat. Every sentence carries information; the only slightly opaque phrase is 'using the same app_id filter as...', which asks the reader to look up the sibling.

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?

There is no output schema, so the description must characterize the return, and it does (buckets, cap of 50, count ordering). What a bucket actually contains and whether the response is a flat list versus nested counts is left unstated, which is the main remaining gap for a faceting endpoint.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: it clarifies that page and page_size are effectively cosmetic for the aggregate result, and that sort does not alter bucket order (buckets stay count-desc). That resolves a likely confusion created by the schema's own 'Result page' and sort descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Discover Steam achievement app ids') and immediately characterizes the return: up to 50 app-id buckets ordered by count. It also names the related sibling `datasets_steam_achievements_search` for the shared app_id filter, though it never explicitly frames itself as the enumeration/faceting counterpart to that search tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: by saying it returns app-id buckets and shares the app_id filter with the search tool, an agent can infer this is for exploring which app ids exist rather than fetching records. There is no explicit 'use this when...' or 'do not use this for...' statement, and no stated prerequisite for calling it.

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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