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datasets_steam_prices_facets

Discover Steam price-history facets by app ID or snapshot date to see which apps or days have the most stored price snapshots.

Instructions

Discover Steam price snapshot values. Returns up to 50 Steam price-history facet buckets ordered by count, using the same filters as datasets_steam_prices_search. The search and snapshot_date facet cover stored history by default; supplying date scopes counts to that day. 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_prices_facets"
  }
}

Input Schema

Name	Required	Description
`date`	No	Snapshot date, yyyy-MM-dd
`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 at all, the description carries the full burden and does disclose meaningful behavior: a 50-bucket cap, count-descending ordering, default history scope for the search and snapshot_date facets, date-scoping semantics, and the fact that page/page_size and sort do not alter the aggregation. It omits safety/read-only framing and any error or rate-limit behavior, which keeps it below a 5.

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 sentences, front-loaded with the return shape before the scoping and pagination caveats. Every sentence carries information, though 'The search and snapshot_date facet cover stored history by default' is slightly tangled for the point it makes.

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 6-parameter, unannotated, no-output-schema facets tool, the description covers return cardinality, ordering, default scope, and pagination behavior — the things an agent most needs. It leaves return field names and any permission/auth expectations unstated, but those are minor for a read-style discovery 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 a 3 is the baseline, but the description adds real meaning beyond the schema: supplying `date` scopes counts to that day rather than widening history, and `sort`/`page`/`page_size` are explicitly declared not to affect bucket counts or order. That resolves ambiguity an agent would otherwise have to guess at from the schema alone.

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 action and output: discover Steam price snapshot values, returning up to 50 facet buckets ordered by count. It also anchors itself to the sibling by saying it uses the same filters as `datasets_steam_prices_search`, which helps separate the facets tool from the search tool. It stops short of explicitly saying 'use this to enumerate distinct values/counts rather than to retrieve records.'

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 implied through 'Discover ... facet buckets' and the pointer to shared filters with `datasets_steam_prices_search`, but there is no explicit when-to-use / when-not statement or a named alternative for retrieving actual price records. The agent must infer that facets are for value enumeration and counting rather than record retrieval.

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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