document.extract_text

Extract plain text from a PDF or image (base64-encoded). Use when you need raw text for downstream AI analysis (summarization, claim checking, structured extraction). For documents at a public URL, use url.extract instead (no base64 encoding needed). Returns: { pages: number, text: string } Example prompts: - "Extract the text from this scanned contract so I can search it." - "Give me the raw text from this PDF document." - "OCR this image and return the text content."

Parameters2

document_base64	string	required	Base64-encoded PDF or image bytes (max ~15 MB). Example: "JVBERi0xLjcNJeLjz9MNCj..." (truncated PDF base64)
mime_type	string	required	MIME type of the document. Example: "application/pdf" for PDFs, "image/png" for PNG screenshots.

Raw schema

{
  "type": "object",
  "properties": {
    "document_base64": {
      "type": "string",
      "description": "Base64-encoded PDF or image bytes (max ~15 MB). Example: \"JVBERi0xLjcNJeLjz9MNCj...\" (truncated PDF base64)"
    },
    "mime_type": {
      "type": "string",
      "enum": [
        "application/pdf",
        "image/jpeg",
        "image/png",
        "image/webp"
      ],
      "description": "MIME type of the document. Example: \"application/pdf\" for PDFs, \"image/png\" for PNG screenshots."
    }
  },
  "required": [
    "document_base64",
    "mime_type"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

document.extract_tables

Extract tables and forms as Markdown from a PDF or image (base64-encoded). Use when the document contains structured tabular data such as financial statements, data sheets, or forms. For plain prose documents, use document.extract_text instead. Returns: { pages: number, text: string } — text contains Markdown-formatted tables. Example prompts: - "Extract the tables from this financial statement." - "Pull the data table from this PDF into Markdown format." - "Get the tabular data from this form document."

Parameters2

document_base64	string	required	Base64-encoded PDF or image bytes (max ~15 MB). Example: "JVBERi0xLjcNJeLjz9MNCj..." (truncated PDF base64)
mime_type	string	required	MIME type of the document. Example: "application/pdf" for PDF bank statements, "image/jpeg" for photo of a form.

Raw schema

{
  "type": "object",
  "properties": {
    "document_base64": {
      "type": "string",
      "description": "Base64-encoded PDF or image bytes (max ~15 MB). Example: \"JVBERi0xLjcNJeLjz9MNCj...\" (truncated PDF base64)"
    },
    "mime_type": {
      "type": "string",
      "enum": [
        "application/pdf",
        "image/jpeg",
        "image/png",
        "image/webp"
      ],
      "description": "MIME type of the document. Example: \"application/pdf\" for PDF bank statements, \"image/jpeg\" for photo of a form."
    }
  },
  "required": [
    "document_base64",
    "mime_type"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

document.parse_invoice

Parse a receipt or invoice document into structured fields. Uses a quality AI model for accuracy. Use when you need to extract line items, totals, and merchant info from financial documents. For general document text, use document.extract_text instead. Returns: { invoice: { merchant, date (YYYY-MM-DD), line_items[], subtotal, tax, total }, cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Parse this invoice and give me the line items and total." - "Extract the merchant, date, and amounts from this receipt." - "Read this scanned invoice and return structured data."

Parameters2

document_base64	string	required	Base64-encoded PDF or image of the receipt/invoice (max ~15 MB). Example: "JVBERi0xLjcNJeLjz9MNCj..." (base64-encoded invoice PDF)
mime_type	string	required	MIME type of the document. Example: "application/pdf" for scanned invoice PDF, "image/jpeg" for a receipt photo.

Raw schema

{
  "type": "object",
  "properties": {
    "document_base64": {
      "type": "string",
      "description": "Base64-encoded PDF or image of the receipt/invoice (max ~15 MB). Example: \"JVBERi0xLjcNJeLjz9MNCj...\" (base64-encoded invoice PDF)"
    },
    "mime_type": {
      "type": "string",
      "enum": [
        "application/pdf",
        "image/jpeg",
        "image/png",
        "image/webp"
      ],
      "description": "MIME type of the document. Example: \"application/pdf\" for scanned invoice PDF, \"image/jpeg\" for a receipt photo."
    }
  },
  "required": [
    "document_base64",
    "mime_type"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

document.check_claims

Verify a list of factual claims against document text. Uses a quality AI model with citation-level evidence. Use after document.extract_text or url.extract when you need to validate specific factual assertions. For open-ended questions about a document, use url.qa instead. For multi-document investigation, use collection.ask. Typical workflow: document.extract_text/url.extract → document.check_claims. Returns: { claims: [{ claim, status: "supported"|"contradicted"|"not_found", evidence: { quote, paragraphs[] }, confidence: "high"|"medium"|"low" }], truncated: boolean } Example prompts: - "Check whether this contract mentions a liability cap of $1M." - "Verify these claims against the document: [claims list]." - "Does the report actually say revenue grew 23%?"

Parameters3

text	string	required	Document text to check claims against. Obtain via document.extract_text or url.extract. Example: "ACME Corp was founded in 2010. Revenue exceeded $1M in 2024."
claims	array	required	Factual statements to verify. Each claim is checked independently against the text. Example: ["Founded in 2010", "Revenue exceeded $1M"]
max_tokens	number	optional	Input length cap (1 token ≈ 4 chars). Default ~3000 tokens. Truncates input text, not the output. Example: 4000

Raw schema

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "description": "Document text to check claims against. Obtain via document.extract_text or url.extract. Example: \"ACME Corp was founded in 2010. Revenue exceeded $1M in 2024.\""
    },
    "claims": {
      "minItems": 1,
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Factual statements to verify. Each claim is checked independently against the text. Example: [\"Founded in 2010\", \"Revenue exceeded $1M\"]"
    },
    "max_tokens": {
      "description": "Input length cap (1 token ≈ 4 chars). Default ~3000 tokens. Truncates input text, not the output. Example: 4000",
      "type": "number"
    }
  },
  "required": [
    "text",
    "claims"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

document.extract_structured

Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, document.parse_invoice (prebuilt schema) may be simpler. For general summarization, use document.summarize instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."

Parameters3

text	string	required	Document text to extract from. Obtain via document.extract_text or url.extract. Example: "This Service Agreement is entered into on 2025-03-15 between ACME Corp and Beta Inc..."
schema	object	required	Field map: describe each field you want extracted with a type hint. Example: { "total_usd": "number", "vendor": "string", "invoice_date": "ISO date YYYY-MM-DD" }
max_tokens	number	optional	Input length cap (1 token ≈ 4 chars). Default ~2500 tokens. Truncates input, not output. Example: 3000

Raw schema

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "description": "Document text to extract from. Obtain via document.extract_text or url.extract. Example: \"This Service Agreement is entered into on 2025-03-15 between ACME Corp and Beta Inc...\""
    },
    "schema": {
      "type": "object",
      "propertyNames": {
        "type": "string"
      },
      "additionalProperties": {},
      "description": "Field map: describe each field you want extracted with a type hint. Example: { \"total_usd\": \"number\", \"vendor\": \"string\", \"invoice_date\": \"ISO date YYYY-MM-DD\" }"
    },
    "max_tokens": {
      "description": "Input length cap (1 token ≈ 4 chars). Default ~2500 tokens. Truncates input, not output. Example: 3000",
      "type": "number"
    }
  },
  "required": [
    "text",
    "schema"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

document.summarize

Summarize document text into a prose summary and key points with citations. Use after document.extract_text or url.extract when you need a condensed understanding of a long document. For single-sentence Q&A, use url.qa instead. For extracting specific fields, use document.extract_structured. Typical workflow: document.extract_text/url.extract → document.summarize. Returns: { summary: string, key_points: string[], summary_cited: { value, confidence, citations[] }, key_points_cited: [{ text, citations[] }], truncated: boolean, strategy: "full"|"truncated"|"chunked" } Example prompts: - "Summarize this financial report and give me the key points." - "What are the main takeaways from this document?" - "Give me a concise summary of this 50-page report."

Parameters2

text	string	required	Document text to summarize. Obtain via document.extract_text or url.extract. Example: "The Q4 2025 financial report shows revenue growth of 23% year-over-year..."
max_tokens	number	optional	Input length cap (1 token ≈ 4 chars). Default ~3000 tokens. Truncates input, not output. Example: 4000

Raw schema

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "description": "Document text to summarize. Obtain via document.extract_text or url.extract. Example: \"The Q4 2025 financial report shows revenue growth of 23% year-over-year...\""
    },
    "max_tokens": {
      "description": "Input length cap (1 token ≈ 4 chars). Default ~3000 tokens. Truncates input, not output. Example: 4000",
      "type": "number"
    }
  },
  "required": [
    "text"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

bundle.verify

Verify the cryptographic integrity of an evidence bundle (ev_...) owned by your API key. Checks manifest hash, EIP-191 signature, and R2 artifact hashes. Free — no credits consumed. Use when you need to confirm a bundle has not been tampered with. For quick metadata lookups (without full crypto verification), use bundle.get instead. Also returns a signed action receipt (rcpt_...) binding this verify call to the bundle manifest — list with receipt.list, verify with receipt.verify. Returns: { valid: boolean, bundle_id, manifest_sha256, checks: { status, manifest_hash, signature, artifacts: [{ name, ok }] }, tampered: string[], signer_address: string|null, attestation_tx: string|null, url: string, captured_at: string, receipt: ActionReceipt|null } Example prompts: - "Verify the cryptographic integrity of bundle ev_550e8400." - "Is this evidence bundle still valid and untampered?" - "Deep-check the manifest hash and signature of my bundle."

Parameters1

bundle_id

string

required

Evidence bundle ID (ev_...) returned by extract or notarize. Example: "ev_550e8400-e29b-41d4-a716-446655440000"

Raw schema

{
  "type": "object",
  "properties": {
    "bundle_id": {
      "type": "string",
      "description": "Evidence bundle ID (ev_...) returned by extract or notarize. Example: \"ev_550e8400-e29b-41d4-a716-446655440000\""
    }
  },
  "required": [
    "bundle_id"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

collection.create

Create a named document collection for cross-document semantic search and RAG-based Q&A. Free — no credits consumed. Use when you want to group related evidence bundles for unified search (collection.search) or question answering (collection.ask). NOTE: Collections start empty. Add evidence bundles with collection.add_document. Indexing is async — once complete, use collection.search or collection.ask. Returns: { collection_id: string (col_...), name: string } Example prompts: - "Create a collection called Q4 Contracts for my quarterly reports." - "Set up a new document group named Due Diligence Docs." - "Make a collection to organize my vendor agreements."

Parameters1

name

string

required

Human-readable collection name. Example: "Q4 Contracts" or "Due Diligence Docs"

Raw schema

{
  "type": "object",
  "properties": {
    "name": {
      "type": "string",
      "description": "Human-readable collection name. Example: \"Q4 Contracts\" or \"Due Diligence Docs\""
    }
  },
  "required": [
    "name"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

collection.search

Semantic (vector) search across documents in a collection. Returns ranked text chunks with relevance scores. Free — no credits consumed. Use when you need raw matching chunks from a collection. For a synthesized cited answer from the same context, use collection.ask instead. PREREQUISITE: Collection must be populated via collection.add_document and async indexing must complete (poll job.status) before results appear. Returns: { results: [{ bundle_id, chunk_id, text, score: number (0–1), title? }] } Example prompts: - "Search my Q4 Contracts collection for mentions of liability cap." - "Find the clause about data retention in my due diligence docs." - "Search for revenue numbers across my quarterly reports."

Parameters3

collection_id	string	required	Collection ID (col_...) returned by collection.create. Example: "col_550e8400-e29b-41d4-a716-446655440000"
query	string	required	Natural language search query. Example: "What were the revenue numbers for Q4?"
limit	number	optional	Max chunks to return (default 10, max 50). Example: 5

Raw schema

{
  "type": "object",
  "properties": {
    "collection_id": {
      "type": "string",
      "description": "Collection ID (col_...) returned by collection.create. Example: \"col_550e8400-e29b-41d4-a716-446655440000\""
    },
    "query": {
      "type": "string",
      "description": "Natural language search query. Example: \"What were the revenue numbers for Q4?\""
    },
    "limit": {
      "description": "Max chunks to return (default 10, max 50). Example: 5",
      "type": "number"
    }
  },
  "required": [
    "collection_id",
    "query"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

collection.ask

Answer a question using RAG over a document collection. Retrieves relevant chunks then synthesizes a cited answer with source attribution. Use when you need a direct answer grounded in your collection documents. For raw matching chunks (without synthesis), use collection.search instead. For single-document Q&A, use url.qa instead. PREREQUISITE: Collection must be populated via collection.add_document and indexed before results appear. Returns: { answer: string, sources: [{ bundle_id, chunk_id }], retrieval: [{ bundle_id, chunk_id, text, score }] } Example prompts: - "What are the key terms of the service agreement in my collection?" - "Based on my due diligence docs, what are the main risks?" - "Answer this question using all documents in the Q4 Contracts collection."

Parameters3

collection_id	string	required	Collection ID (col_...) returned by collection.create. Example: "col_550e8400-e29b-41d4-a716-446655440000"
question	string	required	Natural language question to answer from collection documents. Example: "What are the key terms of the service agreement?"
max_chunks	number	optional	Max chunks to retrieve for context (default 8). Increase for broad questions, decrease for precision. Example: 12

Raw schema

{
  "type": "object",
  "properties": {
    "collection_id": {
      "type": "string",
      "description": "Collection ID (col_...) returned by collection.create. Example: \"col_550e8400-e29b-41d4-a716-446655440000\""
    },
    "question": {
      "type": "string",
      "description": "Natural language question to answer from collection documents. Example: \"What are the key terms of the service agreement?\""
    },
    "max_chunks": {
      "description": "Max chunks to retrieve for context (default 8). Increase for broad questions, decrease for precision. Example: 12",
      "type": "number"
    }
  },
  "required": [
    "collection_id",
    "question"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

url.extract

Fetch a public HTTPS URL and return extracted text and page metadata. Lean mode — no evidence bundle stored, no bundle_id returned. Use for raw text extraction from web pages and online documents. Use url.summarize for summaries, url.qa for Q&A, url.translate for translation, document.extract_text for base64 file uploads. Returns: { url, title, word_count, text, final_url (after redirects) } Example prompts: - "Extract the text from https://example.com/report.pdf for me." - "Get me the raw content of this web page: [URL]." - "Pull the text from this online article so I can analyze it."

Parameters1

url

string

required

Public HTTPS URL to fetch and extract. Example: "https://example.com/report.pdf" or "https://blog.example.com/article"

Raw schema

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "format": "uri",
      "description": "Public HTTPS URL to fetch and extract. Example: \"https://example.com/report.pdf\" or \"https://blog.example.com/article\""
    }
  },
  "required": [
    "url"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

url.summarize

Fetch a public HTTPS URL and return a prose summary with key points. Lean mode — no bundle stored. Use when you need a condensed understanding of a web page. For raw text, use url.extract. For asking a specific question about a page, use url.qa. Returns: { url, summary, key_points: string[], truncated: boolean, word_count } Example prompts: - "Summarize https://en.wikipedia.org/wiki/Artificial_intelligence for me." - "Give me the key points from this blog post: [URL]." - "What is this article about? Summarize [URL]."

Parameters2

url	string	required	Public HTTPS URL to fetch and summarize. Example: "https://en.wikipedia.org/wiki/Artificial_intelligence"
max_tokens	number	optional	Input length cap (1 token ≈ 4 chars). Truncates fetched page content, not the output summary. Example: 4000

Raw schema

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "format": "uri",
      "description": "Public HTTPS URL to fetch and summarize. Example: \"https://en.wikipedia.org/wiki/Artificial_intelligence\""
    },
    "max_tokens": {
      "description": "Input length cap (1 token ≈ 4 chars). Truncates fetched page content, not the output summary. Example: 4000",
      "type": "number"
    }
  },
  "required": [
    "url"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

url.qa

Fetch a public HTTPS URL and answer a specific question about its content. Lean mode — no bundle stored. Use when you have a precise question about a web page. For a broad summary, use url.summarize. For multi-document Q&A, use collection.ask instead. Returns: { url, answer, answer_cited: { value, confidence, citations[] }, confidence: "high"|"medium"|"low", truncated } Example prompts: - "What is the refund policy at https://docs.example.com/policy?" - "Look at [URL] and tell me what the delivery terms are." - "Answer this question based on the content of [URL]: [question]."

Parameters3

url	string	required	Public HTTPS URL to fetch and question. Example: "https://docs.example.com/policy"
question	string	required	Specific question to answer from the page content. Example: "What is the refund policy?"
max_tokens	number	optional	Input length cap (1 token ≈ 4 chars). Truncates fetched page content, not the answer. Example: 4000

Raw schema

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "format": "uri",
      "description": "Public HTTPS URL to fetch and question. Example: \"https://docs.example.com/policy\""
    },
    "question": {
      "type": "string",
      "description": "Specific question to answer from the page content. Example: \"What is the refund policy?\""
    },
    "max_tokens": {
      "description": "Input length cap (1 token ≈ 4 chars). Truncates fetched page content, not the answer. Example: 4000",
      "type": "number"
    }
  },
  "required": [
    "url",
    "question"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

url.translate

Fetch a public HTTPS URL and return its content translated into a target language. Lean mode — no bundle stored. Use when you need to understand web content in a different language. For extracting raw untranslated text, use url.extract instead. Returns: { url, translated_text, target_lang, truncated } Example prompts: - "Translate https://example.de/artikel into English for me." - "Translate this German article into Spanish: [URL]." - "Fetch [URL] and give me the French translation."

Parameters3

url	string	required	Public HTTPS URL to fetch and translate. Example: "https://example.de/artikel"
target_lang	string	required	ISO 639-1 language code for the target language. Example: "es" for Spanish, "fr" for French, "de" for German, "ja" for Japanese, "zh" for Chinese
max_tokens	number	optional	Input length cap (1 token ≈ 4 chars). Truncates fetched page content before translation. Example: 4000

Raw schema

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "format": "uri",
      "description": "Public HTTPS URL to fetch and translate. Example: \"https://example.de/artikel\""
    },
    "target_lang": {
      "type": "string",
      "description": "ISO 639-1 language code for the target language. Example: \"es\" for Spanish, \"fr\" for French, \"de\" for German, \"ja\" for Japanese, \"zh\" for Chinese"
    },
    "max_tokens": {
      "description": "Input length cap (1 token ≈ 4 chars). Truncates fetched page content before translation. Example: 4000",
      "type": "number"
    }
  },
  "required": [
    "url",
    "target_lang"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

bundle.get

Retrieve metadata for an evidence bundle (ev_...) owned by your API key. Free — no credits consumed. Use for quick status/metadata lookups such as checking if a bundle is complete, finding its notarization status, or viewing retention/legal hold info. For deep cryptographic integrity verification (hash + signature + artifact checks), use bundle.verify instead. Also returns a signed action receipt (rcpt_...) binding this lookup to the bundle manifest — list with receipt.list, verify with receipt.verify. Returns: { bundle_id, source_url, mode, status: "pending"|"complete"|"failed", manifest_sha256, manifest_signature, signer_address, attestation_tx, attestation_at, eas_uid, parent_bundle_id, superseded_by, legal_hold: boolean, retention_until, created_at, receipt: ActionReceipt|null } Example prompts: - "Show me the metadata for bundle ev_550e8400." - "Check the status and notarization info of my evidence bundle." - "Get me the details of bundle [ev_id] — is it complete?"

Parameters1

bundle_id

string

required

Evidence bundle ID (ev_...) returned by extract or bundle.notarize. Example: "ev_550e8400-e29b-41d4-a716-446655440000"

Raw schema

{
  "type": "object",
  "properties": {
    "bundle_id": {
      "type": "string",
      "description": "Evidence bundle ID (ev_...) returned by extract or bundle.notarize. Example: \"ev_550e8400-e29b-41d4-a716-446655440000\""
    }
  },
  "required": [
    "bundle_id"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

bundle.notarize

Notarize an evidence bundle on-chain by writing its manifest SHA-256 to the blockchain (Base/EVM). Creates a permanent, tamper-evident on-chain record of the document fingerprint. If the bundle is already notarized, returns the existing attestation immediately (idempotent). Use when you need an immutable on-chain timestamp proving a document existed at a point in time. For quick integrity checks without on-chain cost, use bundle.verify instead. Also returns a signed action receipt (rcpt_...) binding this notarize call to the bundle manifest — list with receipt.list, verify with receipt.verify. PREREQUISITE: Bundle status must be "complete". Check status with bundle.get first. NOTE: Costs gas (ETH). The on-chain record is permanent and cannot be deleted even if the bundle is later purged. Returns: { bundle_id, attestation: { tx_hash, network, attested_at, key_id, eas_uid?, schema_uid? }, receipt: ActionReceipt|null } Example prompts: - "Notarize bundle ev_550e8400 on-chain so I have a permanent record." - "Put the fingerprint of my evidence bundle on the blockchain." - "Create an on-chain timestamp for this document bundle."

Parameters1

bundle_id

string

required

Evidence bundle ID (ev_...) to notarize. Bundle must have status "complete". Example: "ev_550e8400-e29b-41d4-a716-446655440000"

Raw schema

{
  "type": "object",
  "properties": {
    "bundle_id": {
      "type": "string",
      "description": "Evidence bundle ID (ev_...) to notarize. Bundle must have status \"complete\". Example: \"ev_550e8400-e29b-41d4-a716-446655440000\""
    }
  },
  "required": [
    "bundle_id"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

receipt.verify

Independently verify a signed action receipt (rcpt_...) returned by bundle.get, bundle.verify, bundle.notarize, collection.add_document, or listed via receipt.list. Free — no credits consumed. Proves both that the receipt signature is authentic AND that the manifest_sha256 it was bound to still matches the bundle's current manifest — i.e. that the action was not performed against a stale or since-superseded document. Use for third-party audit of an agent's prior actions. Returns: { receipt_id, valid: boolean, signature_valid: boolean, manifest_matches_current: boolean, bundle_id, agent_id, action, manifest_sha256, signer_address, signed_at, tampered: string[] } Example prompts: - "Verify action receipt rcpt_550e8400 is authentic and still current." - "Was this receipt signed against the real document, or a stale copy?"

Parameters1

receipt_id

string

required

Action receipt ID (rcpt_...) returned in the receipt field of another tool's response. Example: "rcpt_550e8400-e29b-41d4-a716-446655440000"

Raw schema

{
  "type": "object",
  "properties": {
    "receipt_id": {
      "type": "string",
      "description": "Action receipt ID (rcpt_...) returned in the receipt field of another tool's response. Example: \"rcpt_550e8400-e29b-41d4-a716-446655440000\""
    }
  },
  "required": [
    "receipt_id"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

receipt.list

List signed action receipts (rcpt_...) for an evidence bundle owned by your API key. Free — no credits consumed. Use after bundle.get, bundle.verify, bundle.notarize, or collection.add_document to audit which agent actions were bound to which manifest hash. Pass a receipt_id from the results to receipt.verify for independent signature + manifest-binding verification. Returns: { bundle_id, receipts: [{ receipt_id, bundle_id, agent_id, action, manifest_sha256, signed_at, signature, signer_address, key_id, algorithm }], limit, offset } Example prompts: - "List all signed action receipts for bundle ev_550e8400." - "What agent actions have been recorded against this evidence bundle?" - "Show me the receipts for [bundle_id] so I can verify one."

Parameters3

bundle_id	string	required	Evidence bundle ID (ev_...) to list receipts for. Example: "ev_550e8400-e29b-41d4-a716-446655440000"
limit	number	optional	Max receipts to return (default 50, max 200). Example: 50
offset	number	optional	Pagination offset (default 0). Example: 0

Raw schema

{
  "type": "object",
  "properties": {
    "bundle_id": {
      "type": "string",
      "description": "Evidence bundle ID (ev_...) to list receipts for. Example: \"ev_550e8400-e29b-41d4-a716-446655440000\""
    },
    "limit": {
      "description": "Max receipts to return (default 50, max 200). Example: 50",
      "type": "number"
    },
    "offset": {
      "description": "Pagination offset (default 0). Example: 0",
      "type": "number"
    }
  },
  "required": [
    "bundle_id"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

job.status

Poll the status of an async job (extract, indexing, batch). Free — no credits consumed. Use after collection.add_document or async extract to check when processing completes. Poll this endpoint in a loop until status is "complete" or "failed". Completed jobs include the bundle_id or result_json in the response. Jobs are created when you POST /v1/extract with a webhook, or when collection.add_document triggers async indexing. Returns: { id, type: "extract"|"extract_batch"|"index_collection", status: "queued"|"processing"|"complete"|"failed"|"cancelled", progress_pct: number (0–100), progress_message, bundle_id (when complete), result_json (when complete), error (when failed), created_at, completed_at } Example prompts: - "Check the status of my indexing job job_550e8400." - "Is my async extract job done yet?" - "Poll job [job_id] — what is the current progress?"

Parameters1

job_id

string

required

Job ID (job_...) returned by async extract or collection.add_document. Example: "job_550e8400-e29b-41d4-a716-446655440000"

Raw schema

{
  "type": "object",
  "properties": {
    "job_id": {
      "type": "string",
      "description": "Job ID (job_...) returned by async extract or collection.add_document. Example: \"job_550e8400-e29b-41d4-a716-446655440000\""
    }
  },
  "required": [
    "job_id"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

collection.list

List all document collections owned by your API key. Free — no credits consumed. Use before collection.search or collection.ask when you need the collection ID. Supports pagination with limit and offset. Returns: { collections: [{ id, name, created_at }] } Example prompts: - "List all my document collections." - "Show me the collections I have created." - "What collections do I own? List them."

Parameters2

limit	number	optional	Max collections to return (default 50, max 100). Example: 20
offset	number	optional	Pagination offset (default 0). Example: 0

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "description": "Max collections to return (default 50, max 100). Example: 20",
      "type": "number"
    },
    "offset": {
      "description": "Pagination offset (default 0). Example: 0",
      "type": "number"
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}

collection.add_document

Add an evidence bundle to a collection and trigger async vector indexing. Use after collection.create to populate a collection with documents. Once indexed, documents become searchable via collection.search and collection.ask. Indexing is async — poll job.status with the returned job_id until status is "complete". Also returns a signed action receipt (rcpt_...) binding this add call to the bundle manifest — list with receipt.list, verify with receipt.verify. PREREQUISITE: Bundle must have status "complete" (check with bundle.get). Collection must be owned by your API key. Returns: { collection_id, bundle_id, job_id (poll for indexing completion), receipt: ActionReceipt|null } Example prompts: - "Add my contract bundle ev_550e8400 to the Q4 Contracts collection." - "Put this evidence bundle into my Due Diligence Docs collection for search." - "Add document [bundle_id] to collection [col_id] with a title."

Parameters3

collection_id	string	required	Collection ID (col_...) returned by collection.create. Example: "col_550e8400-e29b-41d4-a716-446655440000"
bundle_id	string	required	Evidence bundle ID (ev_...) to add. Bundle must have status "complete". Example: "ev_550e8400-e29b-41d4-a716-446655440000"
title	string	optional	Optional display title for the document in this collection. Example: "Q4 2025 Financial Report"

Raw schema

{
  "type": "object",
  "properties": {
    "collection_id": {
      "type": "string",
      "description": "Collection ID (col_...) returned by collection.create. Example: \"col_550e8400-e29b-41d4-a716-446655440000\""
    },
    "bundle_id": {
      "type": "string",
      "description": "Evidence bundle ID (ev_...) to add. Bundle must have status \"complete\". Example: \"ev_550e8400-e29b-41d4-a716-446655440000\""
    },
    "title": {
      "description": "Optional display title for the document in this collection. Example: \"Q4 2025 Financial Report\"",
      "type": "string"
    }
  },
  "required": [
    "collection_id",
    "bundle_id"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

account.quota

Get current credit balance and plan details for your API key. Free — no credits consumed. Check this before running credit-consuming operations (extract, summarize, etc.) to avoid QUOTA_EXCEEDED errors. Returns plan tier, billing period, and usage breakdown. Returns: { plan_id, billing_period (YYYY-MM), credits_used, credits_limit, credits_remaining, status: "active"|"suspended" } Example prompts: - "How many credits do I have left this month?" - "Check my current quota and plan status." - "Am I going to hit my credit limit soon?"

Parameters

No parameters.

Raw schema

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}

com.docimprint/api

MCP Server: com.docimprint/api

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

document.extract_text

document.extract_tables

document.parse_invoice

document.check_claims

document.extract_structured

document.summarize

bundle.verify

collection.create

collection.search

collection.ask

url.extract

url.summarize

url.qa

url.translate

bundle.get

bundle.notarize

receipt.verify

receipt.list

job.status

collection.list

collection.add_document

account.quota

Install

Remote endpoint

Related servers