Skip to main content
Glama
pdfdotco

PDF.co MCP Server

Official
by pdfdotco

pdf_make_searchable

Convert scanned PDFs and images into text-searchable documents by running OCR and adding an invisible text layer for full text search.

Instructions

Convert scanned PDF documents or image files into a text-searchable PDF.
Runs OCR and adds an invisible text layer that can be used for text search.
Ref: https://developer.pdf.co/api-reference/pdf-change-text-searchable/searchable.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)
langNoLanguage for OCR for scanned documents. Default is 'eng'. See PDF.co docs for supported languages. (Optional, Default: 'eng')eng
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

  • Helper service function that actually makes the API call to the PDF.co '/v1/pdf/makesearchable' endpoint.
    async def make_pdf_searchable(
        params: ConversionParams, api_key: str | None = None
    ) -> BaseResponse:
        return await request("pdf/makesearchable", params, api_key=api_key)
  • Core 'request' helper that builds the HTTP payload, sends it via PDFCoClient to the PDF.co API, and returns 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?

No annotations provided. Description mentions adding invisible text layer but omits behavioral traits like processing time, limitations, or side effects. For a tool with no annotations, this is insufficient.

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 concise sentences with a reference link. Front-loaded with purpose. Could include more useful details without being verbose, but current length is appropriate.

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?

Tool has 8 parameters and no output schema, yet description does not explain output format, handling of image files, or limitations. It relies on external reference, which is not part of the description.

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 baseline 3. Description does not add extra parameter info beyond schema, but schema already details each parameter clearly.

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 tool converts scanned PDFs or images into searchable PDFs using OCR, specifying verb and resource. It distinguishes from siblings like pdf_make_unsearchable.

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

Usage Guidelines3/5

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

It implies usage for OCR on scanned documents but does not explicitly state when to use vs alternatives like pdf_make_unsearchable or other pdf tools. No exclusions or preconditions given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/pdfdotco/pdfco-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server