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A camping Call for your AI

one_call
Read-onlyIdempotent

Hands your AI one decision from a checked Dispersed 3D run (the same one for everyone on a date): where it is, the gas, water and light line, and the moves on the menu. Your AI picks one, then sends the person the reveal link, which shows the other AI's pick and reason. The other AI's answer is not in this result. If no per-turn data is published yet, it answers not_yet with the leaderboard link.

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": "one_call"
  }
}

Input Schema

NameRequiredDescriptionDefault
challengeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior4/5

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

The annotations already establish read-only, idempotent, open-world, non-destructive behavior. The description adds useful context beyond that: the decision is shared for everyone on a date, the other AI's answer is withheld, and a not_yet fallback includes the leaderboard link.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably short and front-loads the main action, but it is weighed down by unexplained domain jargon and awkward phrasing such as 'the gas, water and light line'. It is not wasteful, but it is not as clear as its length suggests.

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?

It does explain the returned decision fields and the no-data case, which matters because there is no output schema. However, it leaves the only input parameter undocumented and does not clarify how the agent should send or use the reveal link in practice.

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?

There is one optional parameter, 'challenge', with 0% schema description coverage, and the description never mentions or explains it. An agent would have to guess what value, if any, to supply for this field.

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

Purpose3/5

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

The description says it hands the AI one decision and lists the decision contents, so the verb is clear enough. However, the core concept is expressed in undefined jargon like 'checked Dispersed 3D run' and 'moves on the menu', and it never distinguishes itself from the camping and leaderboard siblings.

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

Usage Guidelines2/5

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

It describes a workflow after the tool returns: your AI picks one and sends the reveal link. It does not state when to use this tool instead of camp_detail, game_leaderboard, or the other siblings, and gives no exclusions or prerequisites.

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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