ai-workers-mcp

ai_proofread

Proofread plain text and receive a list of corrections using a free language model. Ideal for refining prose without processing code or sensitive data.

Instructions

Proofread plain text and list corrections using a configured free LLM. Plain prose only — no code, secrets, or file paths.

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_proofread"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	Plain text to proofread

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Jun 14, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It discloses that a free LLM is used, implying third-party dependency and potential rate limits. It clarifies that the tool only lists corrections (no modification). It could mention if the tool is stateful or idempotent, but overall is transparent.

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?

Two concise sentences with no wasted words. The purpose is front-loaded, and the usage constraint is immediately provided. Every sentence contributes value.

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?

Given the tool's simplicity (1 param, no output schema, no annotations), the description covers essential aspects: input type, constraints, and expected output. It does not specify output format or max input length, but these are minor gaps for a proofreading tool.

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 sole parameter 'text' has schema description 'Plain text to proofread', which matches the tool description. Since schema coverage is 100%, baseline is 3. The description adds no additional semantic detail beyond what the schema provides.

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: 'Proofread plain text and list corrections' using a configured free LLM. This is a specific verb+resource combination, and it distinguishes itself from sibling tools like ai_rewrite or ai_summarize.

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 provides explicit usage guidance: 'Plain prose only — no code, secrets, or file paths.' This tells the agent when not to use the tool, but it does not mention alternative tools for non-plain-text inputs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server