Japan Public Ledgers MCP

content_authenticity_ai_likelihood

Heuristic likelihood (0-100) that a passage of text is AI-generated, from lexical-diversity and burstiness signals with a transparent rationale. Pure, no network; price 0.0 (free).

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": {
      "text": "<text>"
    },
    "name": "content_authenticity_ai_likelihood"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	Text passage to score

Schema Changelog

Changes observed during successful MCP inspections.

AddedJul 17, 2026

TDQS

A3.7/5.0

Behavior3/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions the tool uses lexical-diversity and burstiness signals, provides a transparent rationale, and is free with no network calls. However, it does not describe the return format, idempotency, or any potential side effects, which are needed for a tool with no output schema.

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 concise at 28 words across two sentences. It front-loads the core purpose and key constraints (free, no network), with no unnecessary words or repetition.

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?

Given the lack of an output schema, the description should clarify the return value. It mentions a 'likelihood (0-100)' and 'transparent rationale', but does not explicitly state the output structure (e.g., object with score and explanation). The tool's simplicity mitigates this, but completeness could be improved.

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

Parameters3/5

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

The input schema has 100% description coverage for the single 'text' parameter, with a clear description 'Text passage to score'. The description reinforces this but does not add significant new semantic information beyond what the schema provides, so a baseline score of 3 is appropriate.

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 purpose: assessing the heuristic likelihood (0-100) that a text passage is AI-generated, using lexical-diversity and burstiness signals. It is distinct from sibling content_authenticity tools that deal with C2PA, domain reputation, provenance, and watermark detection, which are for different media types and methods.

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 implies the tool is suitable for scoring text passages and is free with no network calls, but it does not explicitly state when to use this tool over alternatives or when not to use it. No contrast with other tools is provided, leaving the agent to infer usage from purpose alone.

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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