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ai_rewrite

Rewrite a text in another tone, keeping its meaning, facts and language: professional (default), casual, friendly, formal, concise, simple, persuasive, confident or empathetic. Up to 5,000 characters. Llama 4 Scout on Workers AI. Costs $0.003 USDC on Base via x402.

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": "ai_rewrite"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesThe text, up to 5,000 characters
styleNoTarget tone (default professional)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full disclosure burden and does well: it names the backing model (Llama 4 Scout on Workers AI), states an explicit 5,000-character ceiling, and — most importantly — discloses that invocation costs $0.003 USDC on Base via x402, which an agent must know before calling. It omits whether the call is idempotent, expected latency, and what happens to text that exceeds the limit.

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?

The core capability and its constraints are front-loaded in the first two clauses, with operational facts (model, price, chain, protocol) appended. It is dense but every clause carries information; only the pricing/model tail risks being skimmed as boilerplate.

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

Completeness4/5

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

For a two-parameter, single-purpose text tool with no output schema, the description supplies the important operational context: cost, payment rail, model, and input length limit. Missing only the shape of the returned text and over-limit behavior, which are minor given the tool's simplicity.

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?

Schema description coverage is 100%, and both parameters (text with its length constraint, style with a fully enumerated default) are already documented in the schema. The description restates the tone list and the 5,000-character limit, so it adds no syntax or format meaning beyond the structured fields — the baseline 3 applies.

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

Purpose4/5

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

The description gives a specific verb and resource ('Rewrite a text') plus a tightly bounded scope: meaning, facts and language are preserved while only tone changes. The parenthetical 'keeping its language' implicitly separates it from ai_translate and ai_summarize, but no sibling is named, so the differentiation is inferential rather than explicit.

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 preservation clause ('keeping its meaning, facts and language') hints at when this is the right choice rather than translation or summarization, but there is no explicit when-to-use/When-not-to-use statement and no named alternative tools. An agent must infer the routing decision.

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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