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Agent Data Tools: AI

by io.github.Nutshell2000
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Rewrite text in a style: rewrite text to be more formal, casual, simpler, shorter or more persuasive

ai_rewrite
Read-only

Rewrite text to be more formal, casual, simpler, shorter, friendlier, more professional or more persuasive, keeping the meaning and language. Use to polish emails, replies, product copy and messages. Example: text=hey, cant make it tmrw, can we move to thu?. Price $0.01/call via x402 USDC or a card key (/credits), first 25 free; failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not 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": "ai_rewrite"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesText, up to 8,000 characters
styleNoformal, casual, simpler, shorter, friendly, professional or persuasive (default professional)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, destructiveHint=false and idempotentHint=false; the description reinforces the non-idempotency with an operational reason ('each call is charged, so repeats are not free'). It also discloses cost, billing paths (x402 USDC or card key /credits), the 25-free allowance, and that failed calls are free — real behavioral context annotations cannot express. Return-format detail is the only gap, and no output schema exists.

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?

Purpose, styles, use cases, example, then cost/safety in one tight block — front-loaded and free of filler. The pricing and billing clauses are dense but each carries distinct decision-relevant information.

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

Completeness5/5

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

For a simple 2-parameter transformation tool with annotations covering the safety profile and no output schema, nothing needed to invoke it correctly is missing: input shape, example, meaning-preservation guarantee, cost, and repeat-call behavior are all present.

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% (both params documented, enum listed, default professional stated), so the schema already carries parameter meaning. The description only repeats the style list and gives one sample text value; it adds no syntax or edge-case guidance beyond the schema, so baseline 3 applies.

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?

States a specific verb (rewrite) and resource (text) plus the full set of target styles, and adds the invariant 'keeping the meaning and language.' This distinguishes it cleanly from siblings like ai_summarize_text, ai_translate, and ai_chat.

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?

Gives concrete usage contexts ('polish emails, replies, product copy and messages') and a worked input example, so an agent knows when this is the right call. It does not name an alternative or an explicit when-not case (e.g. shortening vs summarizing), so it stops short of 5.

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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