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humanpen-mcp

humanpen-mcp

Official
by humanpen
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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Re-humanize a job's still-flagged passages for free

free_rehumanize

Continue a finished humanize job for free by uploading a fresh detection report; only still-flagged passages are rewritten at no credit cost.

Instructions

Continue a finished humanize job for FREE. Upload a fresh detection report for that job's result; only the passages it still flags are rewritten, at no credit cost. The server enforces strict limits: one free continuation per job, a per-day cap, the report must be of THAT job's own result (>=90% match) and show >=20% AI, and the result must still exist (kept ~7 days). If a limit is not met it returns a clear reason - relay it and stop; do not retry or silently fall back to a paid job. Pass the job_id a prior humanize_document (or check_job) returned.

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": {
      "job_id": "<job_id>",
      "report_path": "<report_path>"
    },
    "name": "free_rehumanize"
  }
}

Input Schema

NameRequiredDescriptionDefault
job_idYesThe finished humanize job to continue (its job_id from humanize_document/check_job)
segmentsNoPer-passage word control for the still-flagged passages (optional), same shape and experimental caveat as humanize_document.segments
output_pathNoWhere to write the result; defaults to beside the report
report_pathYesAbsolute path to the fresh Turnitin/iThenticate report for that job's result
instructionsNoExtra requirements for this job
wait_secondsNoHow long to wait before returning a job id (default 55)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.4.0

TDQS

A4.9/5.0
Behavior5/5

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

Goes well past the annotations by disclosing enforcement rules the agent cannot infer: one free continuation per job, a per-day cap, a >=90% report match requirement, a >=20% AI threshold, and a ~7-day result retention window. It also specifies failure behavior ('returns a clear reason - relay it and stop'), which is the most operationally important trait here.

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?

Front-loads the free-continuation purpose, then constraints, then the failure directive, then the parameter hint — a sensible priority order. Every sentence carries a distinct rule; nothing is redundant with the schema or annotations.

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?

For a 6-parameter, no-output-schema tool, the description covers preconditions, cost, limits, failure handling, and where job_id comes from. There is no output schema, and the description still tells the agent what to expect back (a job id or a clear refusal reason).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds genuine meaning: job_id must come from a prior humanize_document or check_job call, and report_path must be a report of that job's own result. It leaves segments, output_path, and wait_seconds to the schema, which is reasonable since those are already documented.

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?

States a specific verb and resource ('Continue a finished humanize job') plus a distinctive constraint ('for FREE') that separates it from the sibling humanize_document. It also names the input artifact (a fresh detection report) and its effect (only still-flagged passages are rewritten), so an agent can identify the tool without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly scopes usage: only for an already-finished humanize job, requires a report of THAT job's result, and only runs once per job. It also closes off wrong behavior directly ('do not retry or silently fall back to a paid job'), which is precisely the routing decision an agent could get wrong.

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

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