Superseded listing, not a dead server. This entry was imported from an early Glama registration under com.moltlinestudio.mcp; the same server is listed under its official MCP Registry name — use com.moltlinestudio/humanizer. Endpoint unchanged: https://mcp.moltlinestudio.com/humanizer — still live, still free on the free tier. Only this duplicate entry is deprecated.
An instrumented editing loop for AI drafts: ai_tell_scan scores how human a draft reads with quoted evidence, burstiness_report maps sentence rhythm, voice_fingerprint profiles a real person's writing numerically, and the premium humanize_plan / verify_rewrite pair produces a rewrite brief and proves the result moved toward the target voice.
Scan a draft for the measurable tells of AI-generated prose. FREE.
Flags stock phrases (with exact quotes), structural reflexes, uniform sentence rhythm, em-dash overuse, and hedging boilerplate — every flag cites the actual text. Typical input {"text": ""} returns {"reads_human_score": 0-100, "metrics": {"burstiness": ..., "avg_sentence_len": ..., ...}, "evidence": [{"type": "stock_phrase", "quote": "..."}], "note": "..."}.
Use for a first read on whether a draft carries machine-writing signals. Reports measurable patterns, not a verdict on who wrote the text, and must not be used to accuse a person of anything. Not for rhythm detail (burstiness_report) or for a rewrite brief (humanize_plan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "empty text"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The draft to scan — at least one full sentence of plain text. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint as true. The description reinforces these ('Every call is read-only and idempotent') and adds critical behavioral detail: no protocol errors on invalid input, returns structured error object with fix instructions, and outlines the return format (reads_human_score, metrics, evidence, note). No contradictions.
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?
Every sentence serves a purpose: purpose, flagged patterns, example, usage guidance, siblings, error handling. It is front-loaded and structured. Slightly verbose in listing flags, but each item adds clarity. Overall efficient for the amount of information conveyed.
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's single parameter and the presence of an output schema, the description leaves no gaps. It covers purpose, what it detects, input/output shape, valid input, error behavior, usage guidelines, and sibling distinctions. The output schema exists but the description still summarizes the return structure for quick reference.
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 is only one parameter (text) with 100% schema description coverage. The tool description adds value beyond the schema by including a typical input example and clarifying the minimum requirement ('at least one full sentence'). This extra context helps the agent understand valid usage beyond the schema's basic type constraint.
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 tool's purpose: 'Scan a draft for the measurable tells of AI-generated prose.' It lists specific patterns it flags (stock phrases, structural reflexes, rhythm, em-dash overuse, hedging) and explicitly distinguishes itself from sibling tools burstiness_report and humanize_plan.
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 usage context: 'Use for a first read on whether a draft carries machine-writing signals.' Includes when-not-to-use: 'must not be used to accuse a person,' and names alternatives for related tasks. Also gives error-handling guidance: on invalid input it returns an error object and is always safe to retry.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Map a draft's sentence rhythm and find where it goes flat. FREE.
Reports per-sentence lengths, the burstiness coefficient, and runs of similar-length sentences. Typical input {"text": ""} returns {"sentence_lengths": [12, 14, 13, 5, 28], "burstiness": 0.52, "flat_runs": [{"sentences": "1-3", "lengths": [12, 14, 13]}], "tip": "..."}.
Use when prose reads flat and sentence-length pattern is the suspect. Not for a full inventory of tells (ai_tell_scan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need 3+ sentences"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The draft to analyze; needs at least 3 sentences. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as true/false accordingly. The description goes beyond these by detailing the error handling behavior (never raises protocol error, returns error object), the specific input format, and the retry advice, providing comprehensive behavioral context.
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 concise and well-structured: it starts with a clear purpose, includes a usage note, provides a typical input/output example, explains when to use (and when not), and describes error handling. Every sentence adds value with no redundancy.
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 only one parameter, full schema coverage, and all annotations present, the description is complete. It covers purpose, usage, input/output example, error handling, and read-only/idempotent nature.
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 coverage is 100%, so the schema documents the single parameter well. The description adds value by specifying the typical input format and the requirement for at least 3 sentences, which is already in the schema description but is reinforced with example structure.
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 tool's purpose: to map a draft's sentence rhythm and identify flat spots. It explicitly distinguishes from the sibling tool 'ai_tell_scan' by noting this tool is not for a full inventory of tells.
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 explicit guidance on when to use the tool ('when prose reads flat and sentence-length pattern is the suspect') and when not to use it ('not for a full inventory of tells'), including the name of the alternative tool. It also gives retry instructions after errors.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Produce a precise rewrite brief that de-AIs a draft, with numeric targets. PREMIUM (license).
Lists every flagged tell with its fix and sentence-rhythm surgery targets; when a voice_fingerprint result is supplied, adds numeric targets to hit that person's voice. Apply the brief with your agent, then confirm with verify_rewrite. Typical input {"text": "", "fingerprint": } returns {"current_score": 0-100, "edit_list": ["..."], "numeric_targets": {...}, "process": ..., "integrity_note": ...}.
Use after a scan has identified what to fix; returns a brief, not rewritten prose. Not for checking whether a rewrite worked (verify_rewrite). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The AI draft to plan a rewrite for. | |
| fingerprint | No | Optional voice profile object exactly as returned by voice_fingerprint; omit for a style-only pass. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses error handling behavior: 'never raises a protocol error — it returns {"error": ...}' and confirms read-only, idempotent nature. This adds value beyond annotations (readOnlyHint, idempotentHint) by explaining retry safety and error format. No contradiction with 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 well-structured: starts with a crisp purpose sentence, then details output, usage, error handling, and safety. Every sentence adds necessary information without redundancy. 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?
Given the tool has nested parameters and an output schema, the description covers input/output format, error handling, usage context, sibling relationships, and license restrictions. It is complete enough for an agent to select and invoke correctly.
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 coverage is 100%, but the description adds significant context: typical input example, relationship between text and fingerprint parameters, and expected output structure. This helps the agent understand how to supply the fingerprint parameter and what to expect in return.
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 tool's purpose: 'Produce a precise rewrite brief that de-AIs a draft, with numeric targets.' It distinguishes from siblings by explicitly noting its role after a scan, and contrasting with verify_rewrite for checking rewrites.
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 explicit when-to-use guidance: 'Use after a scan has identified what to fix' and what not to use for: 'Not for checking whether a rewrite worked (verify_rewrite).' It also explains the tool returns a brief, not rewritten prose, and mentions retry safety after error correction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify a rewrite actually improved: score delta, meaning check, voice distance. PREMIUM (license).
Compares reads-human score before/after, a meaning-preservation proxy, and (with a fingerprint) numeric distance to the target voice. Typical input {"original": "", "rewrite": ""} returns {"score_before": N, "score_after": N, "score_delta": N, "content_word_retention_pct": N, "remaining_tells": [...], "verdict": "Improved — ship it" | "Marginal — ..."}.
Use only when both the before and the after text are available. Not for scoring a single draft (ai_tell_scan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "both texts must be non-empty"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| rewrite | Yes | The same content after the humanize_plan edits. | |
| original | Yes | The draft before editing. | |
| fingerprint | No | Optional voice profile object exactly as returned by voice_fingerprint, to measure distance to the target voice. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the core safety profile is covered. The description adds behavioral context by stating that errors return a structured error object instead of protocol errors, and that all calls are read-only and idempotent. However, this doesn't go significantly beyond what the annotations plus schema already convey. The description does not contradict any annotation, so no contradiction flag.
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 front-loaded: the first sentence states the core purpose and the premium license. Subsequent sentences provide details on how it works, typical input/output, when to use, and error behavior. Every sentence serves a distinct purpose—no redundancy. It is tightly written for the amount of information conveyed.
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 that the tool has an output schema (context signals indicate one exists) and annotations cover safety, the description effectively completes the picture: it explains the purpose, usage scenarios, alternative tool, expected input/output format, error handling, and retry safety. For a tool with 3 parameters (2 required) and nested objects, this is fully adequate. No gaps remain.
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 goes beyond the schema by showing the exact JSON input format expected (e.g., '{"original": "<draft>", "rewrite": "<edited draft>"}') and the expected output fields (score_before, score_after, score_delta, content_word_retention_pct, remaining_tells, verdict). This adds significant semantic value by linking parameters to the verification logic and output structure.
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 starts with a clear verb phrase 'Verify a rewrite actually improved' and explicitly lists the three dimensions of verification: score delta, meaning check, and voice distance. It distinguishes itself from the sibling tool 'ai_tell_scan' by stating that this tool compares before and after, while that tool is 'for scoring a single draft'. The mention of 'PREMIUM (license)' further clarifies access restrictions.
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 explicitly states when to use this tool: 'Use only when both the before and the after text are available'. It also provides a clear alternative: 'Not for scoring a single draft (ai_tell_scan)'. Additionally, it notes the error behavior: on invalid input, it returns an error object rather than raising a protocol error, and advises that calls are read-only and idempotent so retrying is safe after correcting input.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Build a measurable voice profile from samples of a person's real writing. FREE.
Feed it 2+ samples (emails, posts, essays — 150+ words total) and use the result with humanize_plan / verify_rewrite. Typical input {"samples": ["", ""]} returns {"label": "my-voice", "target_metrics": {"avg_sentence_len": ..., "burstiness": ..., ...}, "favorite_words": [...], "signature_habits": ["..."], "words_analyzed": N}.
Use on samples the person actually wrote, to build a target profile. Not for scoring an unknown draft (ai_tell_scan) and not on text the person did not write. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need 150+ words of real writing across the samples"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| label | No | Optional name for this voice profile. Default "my-voice". | my-voice |
| samples | Yes | List of real writing samples by the same person; 150+ words combined across all samples. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant value beyond annotations. It confirms read-only and idempotent behavior ('Every call is read-only and idempotent'), explains error handling in detail ('never raises a protocol error — it returns {"error": ...}'), and mentions it is FREE. No contradictions with 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 well-structured with front-loaded purpose and key usage. It includes a usage example, return format, differentiation, error behavior, and safety notes. While every sentence adds value, it could be slightly more concise (e.g., condensing the error explanation). Still very good.
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's complexity (2 params, 100% schema coverage, output schema present, complete annotations), the description fully covers input requirements, output structure, error handling, usage scenarios, and side-effect safety. No gaps remain for an agent to select and invoke correctly.
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 coverage is 100% with descriptions for both parameters. The description adds a concrete example of typical input ({"samples": [...]}), explains the 150+ word requirement, and notes the label parameter's default. This enriches understanding beyond the schema.
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: 'Build a measurable voice profile from samples of a person's real writing.' It clearly distinguishes from siblings by naming ai_tell_scan as the tool for scoring unknown drafts, and by directing use with humanize_plan/verify_rewrite.
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: 'Use on samples the person actually wrote.' Also states when not to use: 'Not for scoring an unknown draft (ai_tell_scan) and not on text the person did not write.' Recommends feeding result to sibling tools, giving clear context.
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. Dates show when Glama detected each change.
ai_tell_scanburstiness_reporthumanize_planverify_rewritevoice_fingerprintClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Each tool has a clearly distinct purpose: scanning for tells, mapping rhythm, producing a rewrite plan, verifying rewrites, and building a voice profile. Descriptions explicitly indicate usage boundaries (e.g., 'not for rhythm detail', 'not for scoring a single draft').
Names use snake_case but vary in structure: verb+noun (verify_rewrite, humanize_plan), noun+noun (burstiness_report, voice_fingerprint), and a compound noun (ai_tell_scan). While still readable, the lack of a consistent verb_noun pattern lowers coherence.
Five tools is well-scoped for a specialized humanization editor. Each tool adds a necessary step in the workflow—from analysis to planning to verification—without redundancy or missing functionality.
The tool set covers the full lifecycle: analysis (ai_tell_scan, burstiness_report), targeting (voice_fingerprint), planning (humanize_plan), and verification (verify_rewrite). No obvious gaps; the rewrite execution is delegated to the agent, which is a sensible design choice.