Skip to main content
Glama

humanizer_humanize_text

Read-onlyIdempotent

Rewrite AI-generated text to sound human and reduce AI detection risk. Returns a mechanical rewrite along with instructions for final polishing.

Instructions

Rewrite AI-generated text to sound human, returning the humanized version.

Applies a deterministic mechanical pass (vocabulary swaps, AI-phrase removal, contraction injection, em-dash cleanup) and returns the rewritten text plus a list of remaining issues that the LLM caller should refine for context.

IMPORTANT — for the model calling this tool: the humanized_text field in the response is a starting point, NOT the final answer. After receiving it, YOU must:

  1. Read the polish_instructions field.

  2. Refine the text to fix burstiness (vary sentence length aggressively), add first-person voice if missing, and smooth any awkward edits the mechanical pass introduced.

  3. Present the FINAL polished version to the user as your reply, along with the before/after risk scores so they can see the improvement.

Args: params (HumanizeTextInput): Text, text type, and voice preferences.

Returns: str: JSON with humanized_text (mechanical rewrite), original_score, rewrite_score, applied_changes, and polish_instructions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Discloses deterministic mechanical pass, lists exact transformations (vocabulary swaps, AI-phrase removal, contraction injection, em-dash cleanup), and notes the returned polish_instructions. Aligns with annotations (readOnlyHint, idempotentHint) without contradiction.

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?

Front-loaded with purpose and process; includes necessary post-call instructions. Slightly verbose due to instructions, but every sentence adds value. Could be trimmed slightly.

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?

Given the tool's single parameter and presence of output schema, the description covers the two-step workflow (tool call then agent refinement) and explains the return fields. Sufficient for correct usage.

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 has good per-property descriptions; description merely summarizes 'text, text type, and voice preferences'. Does not add significant meaning beyond the schema, but coverage is high, so baseline 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?

Description states 'Rewrite AI-generated text to sound human', using specific verb-resource pairing. Differentiates from siblings like humanizer_analyze_ai_tells by focusing on rewriting rather than analysis.

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?

Provides explicit post-call instructions for the agent (refine the text). Does not explicitly mention when to avoid this tool or compare to alternatives, but the context is clear enough.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/aousabdo/humanizer-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server