ToHuman rewrites AI-assisted drafts so they read like your own writing — natural rhythm, varied sentences, same meaning. One tool, humanize(text, intensity): it reworks sentence structure, word choice and rhythm while preserving the meaning, with four intensity levels (minimal, subtle, medium, heavy) and up to 2,000 words per call. Bring your own ToHuman API key as an Authorization: Bearer header — the free tier includes 2,500 words/month, no card. Setup guide: https://tohuman.io/tutorials/mcp-s
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap. The tool's purpose—humanizing AI text—is explicit and singular.
The single tool name 'humanize' is a clear, descriptive verb that perfectly matches its function. With only one tool, there are no inconsistent conventions to evaluate.
One tool is slightly below the typical 3-15 range, but it is reasonable for a server that wraps a single-purpose API endpoint. The count appropriately matches the narrow yet well-defined domain.
The server's stated purpose is simply to humanize AI-generated text, and this single tool fully delivers that capability. There are no obvious missing operations within the intended scope.
Rewrite AI-generated text so it reads like natural human writing. Sends the text to the ToHuman API, which reworks sentence structure, word choice, and rhythm while preserving the meaning. Use it on drafts, emails, reports, and posts before publishing. Works best on complete paragraphs (50+ words); max 2000 words per call.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The AI-generated text to humanize. Max 2000 words per call. | |
| intensity | No | How aggressively to rewrite. 'medium' (default) balances naturalness and fidelity; 'heavy' is the deepest rewrite. | medium |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are all false (readOnly, destructive, etc.), so the description carries the burden of behavioral disclosure. It reveals that the tool sends text to an external API, reworks sentence structure, word choice, and rhythm, preserves meaning, and enforces word count limits. These are meaningful behavioral traits beyond the structured annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences of tightly packed information: purpose, mechanism, and usage constraints. It is front-loaded with the core purpose and each sentence adds distinct value without fluff. It is appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity tool with 2 parameters and no output schema, the description covers what the tool does, how it works (external API call), when to use it, and important limits (word count, paragraph length). It doesn't explicitly state the return value, but that is strongly implied and not a critical gap for an agent choosing or invoking the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds a substantive usage nuance not present in the schema: 'Works best on complete paragraphs (50+ words),' which helps agents understand when the tool will perform well. For the intensity parameter, the schema already explains the enum values and default, so no additional description is necessary.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Rewrite') and resource ('AI-generated text') with a specific outcome ('reads like natural human writing'). It also names the underlying API ('ToHuman API'), providing a distinct mechanism. With no sibling tools, no differentiation is needed, and the purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use context: 'Use it on drafts, emails, reports, and posts before publishing.' It adds practical constraints: 'Works best on complete paragraphs (50+ words); max 2000 words per call,' which implicitly warns against very short or overlong input. Lacks an explicit 'when not to use' statement, but the given constraints largely serve that purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
humanize
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