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humanpen-mcp

humanpen-mcp

Official
by humanpen
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TypeScript
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Condense a document

condense_document

Shorten a .docx to a target word count while preserving meaning, structure, and citations; confirm the credit cost first, then save the result beside the source and return its path.

Instructions

Shorten a .docx to a target word count, keeping meaning, structure and citations. Saves the result next to the source and returns its path. COSTS CREDITS at 20 per 1,000 words processed (10 minimum) - say so and get agreement first.

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": {
      "document_path": "<document_path>",
      "max_words": "<max_words>"
    },
    "name": "condense_document"
  }
}

Input Schema

NameRequiredDescriptionDefault
max_wordsYesTarget word count for the whole document
output_pathNoWhere to write the result; defaults to beside the source
instructionsNoExtra requirements for this job
wait_secondsNoHow long to wait before returning a job id (default 55)
document_pathYesAbsolute path to the .docx

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

Adds substantial context beyond annotations: output is saved next to the source, a path is returned, and the operation burns credits at 20/1,000 words with a 10 minimum. Minor gap: it implies a synchronous path return while the wait_seconds parameter suggests a job-id/async mode that the description never explains.

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?

Three tight sentences: what it does, what it produces, then the cost warning. The most decision-critical information (credit cost and consent requirement) is placed where it will be read, with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 5-parameter mutation tool with no output schema, the description covers purpose, output location, and cost—the things an agent most needs. The async job-id flow implied by wait_seconds and the check_job sibling is left to the schema.

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 description coverage is 100%, so parameters are already documented. The description only reinforces max_words and the default output location, adding no syntax, format, or constraint detail beyond the schema. Baseline 3 applies.

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 ('Shorten a .docx to a target word count') plus the invariants it preserves (meaning, structure, citations). This is clearly distinct from siblings like humanize_document, translate_document, and fix_citations.

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?

Gives an explicit operational precondition ('say so and get agreement first') for the credit cost, which is a real gating instruction. It does not explicitly name alternatives or when-not-to-use, but the credit caveat is a strong usage signal.

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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