AIDataParser
PDFs, images and messy text to schema-guaranteed JSON. try_parse runs a real extraction, no key.
| Endpoint | https://aidataparser.com/v1/mcp (streamable-http) |
|---|---|
| Registry version | 0.2.0 |
| Now | 9 tools · grade D (64) · pin ts_4d6cb35535190f1bfb1890f25b55494f · protocol 2025-06-18 |
| First / last listed | 2026-11-15 00:13 UTC / 2026-11-15 00:13 UTC |
| Grade over time | 2026-11-15: D (64), 9 tools |
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Tools: try_parse create_api_key parse_document parse_text infer_schema validate check_credits list_schemas buy_credits
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 result. Returns the same structured data, confidence and review_needed flag the paid tools return. Lim params: text*, schema, schema_id, instructions |
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 is returned ONCE and is never shown again, so surface it to the user and store it. Ask the user for t params: email* |
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 successful call. params: url, base64, media_type, schema, schema_id, instructions, redact |
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 output shape and `instructions` guides extraction. Returns the extracted data plus a confidence score an params: text*, schema, schema_id, instructions, redact |
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 flat field list and an inferred doc_type. Costs 1 credit per successful call. params: text*, doc_type, instructions |
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. Deterministic, free, and needs no API key. params: data*, schema, schema_id |
check_credits | Return the number of extraction credits remaining on the authenticated API key. Free — does not consume a credit. params: none |
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. params: none |
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. Without a key, pass the user's `email` (ask them; do not invent one) — the page after payment shows the params: pack, email |
Entries describe changes in tool definitions. They are not statements about a server, its quality or its author. The grade measures how the tool definitions fit model providers (token cost, schema compatibility, confusable names) as scored by the ToolFit engine at the time of the listing. JSON: append .json. Opt-out: how or hello@youraitools.co.