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PDF.co MCP Server

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by pdfdotco

pdf_to_csv

Convert PDF and scanned documents to CSV files, preserving layout and table structure for data extraction.

Instructions

Convert PDF and scanned images into CSV representation with layout, columns, rows, and tables.
Ref: https://developer.pdf.co/api-reference/pdf-to-csv.md

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL to the source file. Supports publicly accessible links including Google Drive, Dropbox, PDF.co Built-In Files Storage. Use 'upload_file' tool to upload local files.
httpusernameNoHTTP auth user name if required to access source url. (Optional)
httppasswordNoHTTP auth password if required to access source url. (Optional)
pagesNoComma-separated page indices (e.g., '0, 1, 2-' or '1, 3-7'). Use '!' for inverted page numbers (e.g., '!0' for last page). Processes all pages if None. (Optional)
unwrapNoUnwrap lines into a single line within table cells when lineGrouping is enabled. Must be true or false. (Optional)
rectNoDefines coordinates for extraction (e.g., '51.8,114.8,235.5,204.0'). (Optional)
langNoLanguage for OCR for scanned documents. Default is 'eng'. See PDF.co docs for supported languages. (Optional, Default: 'eng')eng
line_groupingNoEnables line grouping within table cells when set to '1'. (Optional)0
passwordNoPassword of the PDF file. (Optional)
nameNoFile name for the generated output. (Optional)
api_keyNoPDF.co API key. If not provided, will use X_API_KEY environment variable. (Optional)

Implementation Reference

  • Helper function convert_to - builds the API endpoint path and delegates to request(). For pdf_to_csv, calls request("pdf/convert/to/csv", ...)
    async def convert_to(
        _from: str, _to: str, params: ConversionParams, api_key: str | None = None
    ) -> BaseResponse:
        return await request(f"{_from}/convert/to/{_to}", params, api_key=api_key)
  • request function - handles the actual HTTP API call to PDF.co via the PDFCoClient, parsing payload and returning a BaseResponse
    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?

With no annotations, the description bears full burden. It mentions 'scanned images' but does not disclose OCR behavior, handling of multiple tables, page range effects, or requirements (e.g., internet access). Lacks detail beyond basic conversion.

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?

Two sentences: one stating purpose and a reference link. Front-loaded, no fluff. The reference link is extra but not wasteful.

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 11 parameters and no output schema, the description is too minimal. It does not explain output structure beyond 'layout, columns, rows, and tables', missing details on error handling, limits, or typical usage for a complex conversion tool.

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 coverage is 100% with all parameters described. The description adds no additional parameter meaning beyond what the schema provides, so 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 the verb 'Convert', the resource 'PDF and scanned images', and the output format 'CSV representation with layout, columns, rows, and tables', distinguishing it from siblings like pdf_to_text or pdf_to_json.

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 explicit guidance on when to use pdf_to_csv versus alternatives (e.g., pdf_to_xlsx, pdf_to_json). The description only implies usage for CSV output but does not provide selection criteria or exclusions.

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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