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rankedtoday-mcp

rankedtoday-mcp

by RyanKramer
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

hardware_latency_budget

Estimate hardware-induced delay in reaction tests using refresh rate, touchscreen, and polling inputs to separate equipment latency from your true reaction speed.

Instructions

Estimate how much of a reaction-test score is equipment rather than you.

A reaction test measures the whole chain: the screen has to draw the stimulus and the input device has to report the response. Only the middle is you, and the rest is worth real milliseconds.

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": "hardware_latency_budget"
  }
}

Input Schema

NameRequiredDescriptionDefault
refresh_hzNo
is_touchscreenNo
mouse_polling_hzNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior2/5

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

With no annotations, the description carries full burden but does not disclose any behavioral traits beyond the general purpose. It doesn't mention that this is a read-only estimation, what inputs are needed, or any limitations. The description only provides conceptual background, not behavioral specifics.

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 three sentences, front-loaded with the main purpose, and the subsequent sentences add valuable context without excess. It is concise and well-structured.

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

Completeness2/5

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

The description gives a clear conceptual overview but lacks practical guidance on parameter usage. With an output schema present, return values are covered, but the description does not explain how the parameters affect the result or what the expected inputs are.

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 input schema has no parameter descriptions, and the tool description does not mention any of the three parameters (refresh_hz, is_touchscreen, mouse_polling_hz). It provides only implicit context about hardware, which is insufficient to understand parameter meanings.

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 uses a specific verb 'Estimate' and identifies the exact object: the hardware contribution to a reaction-test score. It clearly distinguishes from siblings like kda or hours_played.

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?

The description explains the context of a reaction test and the role of hardware, indicating the tool is for isolating equipment effects. It does not explicitly exclude alternatives or mention when not to use, but the context is clear enough.

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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