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PDF.co MCP Server

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by pdfdotco

find_table

Locate tables within PDF pages and return their bounding box coordinates.

Instructions

Find tables in PDF and get their coordinates.
Ref: https://developer.pdf.co/api-reference/pdf-find/table.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.
httpusernameNoHTTP auth user name if required to access source url. (Optional)
httppasswordNoHTTP auth password if required to access source url. (Optional)
pagesNoComma-separated list of page indices (or ranges) to process. Leave empty for all pages. Example: '0,2-5,7-'. The first-page index is 0. (Optional)
passwordNoPassword of the PDF file. (Optional)
api_keyNoPDF.co API key. If not provided, will use X_API_KEY environment variable. (Optional)

Implementation Reference

  • Service-level helper that makes the actual API call to the "pdf/find/table" endpoint via the shared request() function.
    async def find_table_in_pdf(
        params: ConversionParams, api_key: str | None = None
    ) -> BaseResponse:
        return await request("pdf/find/table", params, api_key=api_key)
  • Generic HTTP helper that builds the payload, calls the PDF.co API via PDFCoClient, and returns a BaseResponse with job status info.
    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 are provided, so the description carries full burden. It only states the basic action but does not disclose behavioral traits such as whether the tool is read-only, required permissions, rate limits, or any side effects. The reference to documentation is present but minimal.

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 very concise with a single meaningful sentence and a reference link. It is front-loaded and contains no extraneous words, though it could be slightly more informative.

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

Completeness3/5

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

Given the complexity of PDF table extraction and the lack of an output schema, the description is minimally adequate. It mentions coordinates but omits details like coordinate format (e.g., bounding boxes) or how to interpret results. The reference helps but does not compensate fully.

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 the baseline is 3. The description does not add any additional meaning or context to the parameters beyond what the schema already provides.

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 specifies the action ('Find'), the resource ('tables in PDF'), and the output ('get their coordinates'). It distinguishes from the sibling tool 'find_text', which finds text, and from other conversion tools. The purpose is immediately clear.

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?

There is no guidance on when to use this tool versus alternatives like pdf_to_csv or pdf_to_json, which could also extract table data. No when-to-use or when-not-to-use information is provided.

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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