AI humanizer for MCP clients. Rewrites AI text so it reads naturally and sounds human.
Scored across 2 tools
The two tools serve completely distinct purposes: one for account status and one for text rewriting. There is no overlap or ambiguity between them.
Both tools follow a consistent verb_noun snake_case pattern: get_account_status and humanize_text. The naming is clear and predictable.
With only two tools, the server feels minimal but appropriate for a single-purpose service. It is not extreme, but it falls below the typical 3-15 tool range considered well-scoped.
The tool surface covers the core functionality (humanizing text) and account management (status check). Some minor gaps exist, such as batch processing or configuration options, but the essential workflows are supported.
Check the UndetectedGPT account linked to this API key: remaining word balance, per-request word limit and per-minute rate limit.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, covering the non-mutating nature. The description adds useful context beyond this by clarifying that the account is tied to the API key and that the tool reports balance and rate limits, which helps set agent expectations.
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 a single, front-loaded sentence that starts with the action verb 'Check' and immediately gives the resource and the relevant output fields. There is no filler or redundant phrasing.
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?
Given the tool has no parameters, no output schema, and read-only annotations, the description is sufficient for an agent to invoke it correctly. It names the account context and all reported values, leaving no major operational gap.
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?
There are zero parameters and the input schema is empty, so there is no parameter-level semantics for the description to add. The baseline of 4 applies; the description correctly focuses on the tool's behavior instead.
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 states a specific verb and resource: 'Check the UndetectedGPT account linked to this API key'. It also lists the exact data returned—word balance, per-request word limit, and per-minute rate limit—making it easily distinguishable from the sibling humanize_text.
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?
The description provides clear context for when to use this tool: when an agent needs account quota or rate-limit information. It does not explicitly name alternatives, but the sibling humanize_text is clearly different in purpose, so selection is unlikely to be ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Rewrite AI-generated text so it reads like natural human writing and scores as human on AI detectors (GPTZero, Originality.ai, Copyleaks, Turnitin). Returns only the rewritten text. Spends words from the account balance (1 input word = 1 word).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to humanize. Minimum 5 words; maximum set by the API key (1000 words by default). | |
| tone | No | Writing tone. Omit, or use 'balanced' (equivalent), for the default style with no tone rewrite. | |
| model | No | Humanizer model. Defaults to ghost-2 (latest). | |
| markdown | No | Preserve markdown formatting in the output. Defaults to true. | |
| spelling | No | English spelling variant for the output. Defaults to us. English text only. | |
| ultra_stealth | No | Stronger restructuring for maximum detector evasion. Ignored when tone is conversational, formal or creative. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no substantive behavioral hints, so the description carries the transparency burden. It discloses a key side effect ('Spends words from the account balance (1 input word = 1 word)') and the output behavior ('Returns only the rewritten text'). It does not mention rate limits or error behavior, but the most important operational trait is clearly stated.
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?
Three sentences with no filler: purpose first, output behavior second, cost implication third. Every sentence earns its place and the most important information is front-loaded.
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?
The description plus the rich input schema is sufficient for an agent to call the tool correctly. It states the return contract, the word-cost side effect, and the intended use, while all parameter defaults and enums are covered by the schema. A pointer to get_account_status for balance checks would be a small improvement, but it is not essential.
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 every parameter is already documented in the input schema. The description adds the account-balance cost model and confirms the return format, but it does not meaningfully extend the meaning of individual parameters beyond what the schema provides. Baseline 3 is appropriate.
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 uses a specific verb ('Rewrite') and resource ('AI-generated text') and states the intended outcome: natural human writing that scores as human on AI detectors. It also clarifies the output contract ('Returns only the rewritten text'), which distinguishes it clearly from the only sibling tool, get_account_status.
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?
The description makes the use case explicit: humanize AI-generated text for natural style and detector evasion. It does not list when-not-to-use scenarios, but there is no competing text tool among the siblings, so no exclusion is necessary. The word-cost note also implies a precondition (sufficient account balance).
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.
get_account_statushumanize_textTransform AI-generated text into natural, human-sounding content that bypasses AI detectors.
Rewrite AI-generated text so it reads as human, in a persona's voice.
Text generation over MCP: prose, emails, blog outlines, SQL, humanizing, text diffs, fake data.
Humanize AI-written English. Facts and length kept. Paid per successful run over x402 (testnet).
Add one secure layer between your agents and this server.
