Crawlora MCP

hltb_stats

Read-only

Read HowLongToBeat's public game statistics and return the selected filter labels, total releases, average completion time and rating, playthrough count, per-year and per-platform release counts, the popular-games module, and the site's ten ranked category lists (most completed, most backlogged, most retired, top playing, best rated, worst rated, most speedruns, most reviews, shortest and longest games). Mirrors the site's own GET /api/stats/games request; anonymous, no search token needed. Filters are the site's own game-statistics multi-selects: genre, perspective, flow, platform and release year, each with an include/exclude mode; accepted values are served by hltb_filters. Every counter and ranking is the site's own aggregate, not computed locally, and no user or account data is returned.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "hltb_stats"
  }
}

Input Schema

Name	Required	Description
`flow`	No	Comma-separated flow values from hltb_filters flows
`year`	No	Comma-separated release years from hltb_filters years
`genre`	No	Comma-separated genre values from hltb_filters genres
`platform`	No	Comma-separated platform values from hltb_filters platforms
`perspective`	No	Comma-separated perspective values from hltb_filters perspectives

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 13, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the readOnlyHint/openWorldHint annotations it discloses provenance ('every counter and ranking is the site's own aggregate, not computed locally'), auth posture (anonymous, no token), and a data-boundary guarantee ('no user or account data is returned'). Those are exactly the behavioral facts an agent needs and cannot get from annotations.

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?

Front-loaded with the action and payload, then constraints, then filter semantics — a sensible order with no filler. The long enumeration of all ten ranked lists is somewhat redundant given an output schema exists, but it is dense rather than padded.

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?

For a read-only, parameterless-required aggregate stats tool with five nested filter objects, full schema coverage and an output schema, the description covers purpose, auth, value sourcing, aggregation semantics, and data boundaries. Nothing an agent needs to invoke it correctly is missing.

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%, but the description still adds real meaning the schema lacks: it names the five filter dimensions, explains that each carries an include/exclude 'mode', and states that accepted values are served by hltb_filters. That explains the undescribed 'mode' field and the value-source dependency, lifting it above the baseline 3.

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?

Starts with a concrete verb+resource ('Read HowLongToBeat's public game statistics') and then enumerates the exact payload: filter labels, totals, average completion time/rating, per-year and per-platform counts, the popular module, and ten named ranked lists. This is specific enough that an agent can distinguish it from sibling stat tools 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?

Gives clear operating context: 'anonymous, no search token needed', mirrors GET /api/stats/games, and routes value discovery to hltb_filters. It does not explicitly say when to prefer this over hltb_popular or hltb_game, so it stops short of full when/when-not guidance.

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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