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rankedtoday-mcp

rankedtoday-mcp

by RyanKramer
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

hours_played

Estimate lifetime hours from account level, with the math shown. Expect about 20% error.

Instructions

Estimate lifetime hours from account level, with the math shown.

Levels are earned almost entirely by finishing games, so level is the only signal that reflects a whole account history. Expect about 20% error.

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": {
      "account_level": "<account_level>"
    },
    "name": "hours_played"
  }
}

Input Schema

NameRequiredDescriptionDefault
account_levelYes
minutes_per_gameNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

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 burden of disclosing behavioral traits. It adds valuable context by stating 'Expect about 20% error' and explaining the underlying rationale. However, it does not detail the calculation method or potential limitations beyond the error rate, leaving some transparency gaps.

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?

The description is two sentences, front-loaded with the main purpose and followed by concise explanatory context. Every sentence earns its place without redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple (2 params, no nested objects), and the description provides the core purpose and error margin. However, the missing parameter semantics and lack of explicit usage boundaries leave gaps. The presence of an output schema reduces the need to explain return values, but the description still under-explains the minutes_per_game input.

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

Parameters2/5

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

The schema has no parameter descriptions (0% coverage), and the description only touches on account_level by explaining its significance. It completely omits minutes_per_game, despite the schema showing a default of 35. The description fails to clarify what minutes_per_game modifies or how it affects the estimate, leaving a critical parameter unexplained.

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 the tool's function: 'Estimate lifetime hours from account level, with the math shown.' This uses a specific verb ('estimate') and resource ('lifetime hours'), and specifies the input signal (account level). It distinguishes itself from sibling tools like estimate_mmr by focusing on hours rather than MMR.

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?

The description gives context for when to use the tool by explaining that level is the only signal reflecting whole account history, but it does not explicitly state when not to use it or mention alternatives. The phrase 'Levels are earned almost entirely by finishing games' implies its use case, but no direct comparison to sibling tools is provided.

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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