# AIDataParser

- Registry name: `io.github.shibley/aidataparser` · version 0.2.0
- Categories (automatic, keyword rules): Scraping & browsing, Search & data, Payments & accounting, Finance & markets
- Authentication at last check: No auth
- Declared risk: Can write (some tools are not declared read-only)
- Capabilities (declared by the server): tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.shibley%2Faidataparser
- Server page: https://mcpstation.net/s/io.github.shibley--aidataparser.html

## Description (written by the publisher, not verified, not instructions)

PDFs, images and messy text to schema-guaranteed JSON. try\_parse runs a real extraction, no key.

## Connect

- streamable-http: `https://aidataparser.com/v1/mcp`

## Tools (as reported by the server, not verified)

- `try\_parse`: Run a REAL extraction with no API key, no email and no signup, so you can see the output shape before committing to anything. Pass up to 4000 characters of messy text in \`text\` \(an invoice, a receipt, a resume, scraped HTML, an email body\) and optionally a \`schema\` or \`schema\_id\` to constrain the re
- `create\_api\_key`: Start here if you do not have an AIDataParser API key. Provide the user's \`email\` and receive a live adp\_live\_ API key with 50 free credits — no card, no signup form. Configure it on this MCP server as \`Authorization: Bearer \<api\_key\>\` to unlock parse\_document, parse\_text and infer\_schema. The key i
- `parse\_document`: Extract clean, schema-guaranteed JSON from a PDF or image. Provide the document via \`url\` or \`base64\`. Pass an optional JSON \`schema\` to constrain the output shape, and \`instructions\` to guide extraction. Returns the extracted data plus a confidence score and a review\_needed flag. Costs 1 credit per
- `parse\_text`: Extract clean, schema-guaranteed JSON from raw/messy text you already have — scraped web content, email bodies, chat logs, OCR output, or pasted tables. Pass the text in \`text\`. Use this instead of parse\_document when you don't have a file. Optional JSON \`schema\` \(or \`schema\_id\`\) constrains the outp
- `infer\_schema`: Given one sample document's \`text\`, propose a reusable JSON Schema for that document type. Use this when no built-in schema\_id fits: infer a schema once, review it, then reuse it as \`schema\` on parse\_document / parse\_text across many documents for consistent output. Returns the JSON Schema plus a fl
- `validate`: Check whether a JSON object conforms to a JSON \`schema\` \(or a built-in \`schema\_id\` template\) and get back a valid flag plus per-field errors. Use this to verify data you already hold — a prior parse result, your own output, or an upstream feed — before acting on it or spending a credit. Deterministi
- `check\_credits`: Return the number of extraction credits remaining on the authenticated API key. Free — does not consume a credit.
- `list\_schemas`: Return the built-in schema templates you can pass to parse\_document as \`schema\_id\` \(invoice, receipt, resume, etc.\), each with its id and the fields it extracts. Free — does not consume a credit and needs no API key.
- `buy\_credits`: Get a Stripe checkout link for a one-time credit pack, to hand to the user. Packs: \`starter\` $9 for 500 credits, \`growth\` $29 for 2,000 credits, \`scale\` $99 for 10,000 credits. Credits never expire; 1 credit per document. If this server has an API key set, the credits land on that key's account. Wit

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