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PDF.co MCP Server

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by pdfdotco

pdf_make_unsearchable

Remove the text layer from a PDF to make it non-searchable, preventing text extraction and search functionality.

Instructions

Make existing PDF document non-searchable by removing the text layer from it.
Ref: https://developer.pdf.co/api-reference/pdf-change-text-searchable/unsearchable.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)
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

  • Service function 'make_pdf_unsearchable' that makes the actual API call to the 'pdf/makeunsearchable' endpoint using the PDFCoClient.
    async def make_pdf_unsearchable(
        params: ConversionParams, api_key: str | None = None
    ) -> BaseResponse:
        return await request("pdf/makeunsearchable", params, api_key=api_key)
  • PDFCoClient async context manager that creates an HTTPX AsyncClient with the API key for making requests to the PDF.co API.
    @asynccontextmanager
    async def PDFCoClient(api_key: str | None = None) -> AsyncGenerator[AsyncClient, None]:
        # Use provided API key, fall back to environment variable
        x_api_key = api_key or X_API_KEY
    
        if not x_api_key:
            raise ValueError("""API key is required. Please provide an API key as a parameter or set X_API_KEY in the environment variables.
            
            To get the API key:
            1. Sign up at https://pdf.co
            2. Get the API key from the dashboard
            3. Either set it as an environment variable or provide it as a parameter
            
            Environment variable setup example (.cursor/mcp.json):
            ```json
            {
                "mcpServers": {
                    "pdfco": {
                        "command": "uvx",
                        "args": [
                            "pdfco-mcp"
                        ],
                        "env": {
                            "X_API_KEY": "YOUR_API_KEY"
                        }
                    }
                }
            }
            ```
            
            Or provide the API key as a parameter when calling the tool.
            """)
    
        client = AsyncClient(
            base_url=__BASE_URL,
            headers={
                "x-api-key": x_api_key,
                "User-Agent": f"pdfco-mcp/{__version__}",
            },
        )
        try:
            yield client
        finally:
            await client.aclose()
Behavior2/5

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

Annotations are absent, so description carries full burden. It discloses the destructive action 'removing the text layer' but fails to specify permanent effects, authentication needs (api_key param), error conditions, or that output is likely asynchronous (common in PDF.co tools).

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?

One sentence plus a reference link is concise and front-loaded. However, it lacks structure (e.g., sections for parameters, output) that would improve scannability.

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?

No output schema, so description should explain return value (likely job ID or URL). It omits mention of asynchronous processing, despite sibling tools 'get_job_check' and 'wait_job_completion' suggesting this pattern. Seven parameters demand more context than provided.

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?

Input schema covers 100% of parameters with descriptions. The tool description adds no extra meaning beyond the schema; baseline 3 is appropriate as schema already documents parameters.

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 'Make [...] non-searchable' and the resource 'existing PDF document', with a specific mechanism 'removing the text layer'. This directly differentiates it from its sibling tool 'pdf_make_searchable' (which would add 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 versus alternatives (e.g., pdf_make_searchable). No mention of prerequisites, irreversible effects, or contexts where it's appropriate.

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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