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PDF.co MCP Server

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by pdfdotco

pdf_split

Split a PDF into separate files by specifying page indices or ranges. Supports single pages, sequences, or splitting every page individually.

Instructions

Split a PDF into multiple PDF files using page indexes or page ranges.
Ref: https://developer.pdf.co/api-reference/pdf-split/by-pages.md

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL to the source PDF file. Supports publicly accessible links including Google Drive, Dropbox, PDF.co Built-In Files Storage. Use 'upload_file' tool to upload local files.
pagesYesComma-separated indices of pages (or page ranges) that you want to use. The first-page index is 1. For example: '1,3,5-7' or '1-2,4-'. Use '*' to split every page into separate files.
httpusernameNoHTTP auth user name if required to access source url. (Optional)
httppasswordNoHTTP auth password if required to access source url. (Optional)
passwordNoPassword of the PDF file. (Optional)
nameNoBase file name for the generated output files. (Optional)
api_keyNoPDF.co API key. If not provided, will use X_API_KEY environment variable. (Optional)

Implementation Reference

  • The split_pdf service function that calls the PDF.co API endpoint 'pdf/split' via the request helper. This is the actual API call made by the pdf_split tool.
    async def split_pdf(params: ConversionParams, api_key: str | None = None) -> BaseResponse:
        return await request("pdf/split", params, api_key=api_key)
  • The request helper function that sends HTTP POST requests to the PDF.co API. Used by split_pdf to make the actual API call.
    async def request(
        endpoint: str,
        params: ConversionParams,
        custom_payload: dict | None = None,
        api_key: str | None = None,
    ) -> BaseResponse:
        payload = params.parse_payload(async_mode=True)
        if custom_payload:
            payload.update(custom_payload)
    
        try:
            async with PDFCoClient(api_key=api_key) as client:
                url = f"/v1/{endpoint}"
                print(f"Requesting {url} with payload {payload}", file=sys.stderr)
                response = await client.post(url, json=payload)
                print(f"response: {response}", file=sys.stderr)
                json_data = response.json()
                return BaseResponse(
                    status="working",
                    content=json_data,
                    credits_used=json_data.get("credits"),
                    credits_remaining=json_data.get("remainingCredits"),
                    tips=f"You **should** use the 'wait_job_completion' tool to wait for the job [{json_data.get('jobId')}] to complete if a jobId is present.",
                )
        except Exception as e:
            return BaseResponse(
                status="error",
                content=f"{type(e)}: {[arg for arg in e.args if arg]}",
            )
Behavior2/5

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

No annotations provided; the description does not disclose behavioral traits beyond the basic operation. It omits details on output format, error behavior, file naming, or whether the original is modified. The reference link may contain details, but the description itself lacks transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence with a reference link. It is appropriately front-loaded, though it could be more informative without sacrificing brevity.

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

Completeness2/5

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

Given 7 parameters, no output schema, and no annotations, the description is insufficient. It does not explain return values, error handling, or file naming conventions, leaving significant gaps for an AI agent.

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 well-documented in the schema. The description itself adds no additional meaning beyond the schema; baseline 3 is appropriate.

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 clearly states 'Split a PDF into multiple PDF files using page indexes or page ranges.' The verb 'split' and resource 'PDF' are specific. This distinctly differentiates it from sibling tools like pdf_merge or pdf_to_text.

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

Usage Guidelines2/5

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

No guidance on when to use this tool over alternatives (e.g., pdf_merge) or exclusion criteria. The description simply states what it does without context or prerequisites.

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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