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papyrio-mcp

papyrio-mcp

by atabekdemurtaza
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TypeScript
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split_pdf

Separate every page of a PDF into individual files and return a ZIP archive, so you can extract single pages from long documents.

Instructions

Separate every page of a PDF into its own file, returned as a ZIP archive. Use this to pull single pages out of a long document. The input is uploaded to the Papyrio API, processed in memory and not stored. The result is written to output_path, replacing any file already there, and the call returns that path and the size in bytes. Inputs are limited to 50 MB, and each call counts as one operation against the daily quota.

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": {
      "file": "<file>"
    },
    "name": "split_pdf"
  }
}

Input Schema

NameRequiredDescriptionDefault
fileYesAbsolute path to the PDF to split.
output_pathNoAbsolute path to write the result to. Defaults to the input file with a "-papyrio.zip" suffix.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly: it discloses that input is uploaded to the Papyrio API, processed in memory and not stored, that output_path overwrites any existing file, the 50 MB size limit, and that each call consumes one daily-quota operation. It also states the return payload (path and size in bytes).

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?

The output format and use case are front-loaded, followed by privacy, side effects, limits, and return value in tight sentences. No sentence is filler; the density is high but every clause carries information an agent needs.

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 two-parameter tool with no output schema, the description covers purpose, when to use, side effects, privacy, limits, quota cost, and return shape. Nothing an agent needs to invoke it correctly is missing.

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 schema already defines both parameters. The description goes beyond it by explaining the overwrite behavior of output_path and the shape of the return value, adding operational meaning rather than repeating the schema.

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?

The description opens with a specific verb and resource ('Separate every page of a PDF into its own file, returned as a ZIP archive'), which clearly distinguishes it from siblings like merge_pdfs (the inverse operation) and extract_pdf_text.

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?

'Use this to pull single pages out of a long document' gives a clear use case, and the ZIP output clarifies the deliverable. However, it never explicitly names an alternative (e.g., extract_pdf_text when you only need text) or states when not to use it, so the routing is implied rather than spelled out.

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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