Docimprint API is a remote MCP server that gives an AI agent 22 tools, among them account.quota, bundle.get, bundle.notarize. It answers at https://api.docimprint.com/mcp over streamable HTTP, and it listed its tools to an anonymous client, so no sign-in was needed for that. Our probes reached it 10 of 10 times since 4 September 2026; the last check, on 18 October 2026, succeeded.
AI document intelligence: extract, summarize, claim-check, notarize, and signed action receipts.
Description as the publisher wrote it.
| Tool | What it does |
|---|---|
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, an |
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 Inputs: bundle_id |
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 att Inputs: bundle_id |
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 tampere Inputs: bundle_id |
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 a Inputs: collection_id, bundle_id, title |
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 (wit Inputs: collection_id, question, max_chunks |
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 (collectio Inputs: name |
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, Inputs: limit, offset |
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 c Inputs: collection_id, query, limit |
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 abou Inputs: text, claims, max_tokens |
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_in Inputs: text, schema, max_tokens |
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 inst Inputs: document_base64, mime_type |
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 encodin Inputs: document_base64, mime_type |
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_t Inputs: document_base64, mime_type |
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 extracti Inputs: text, max_tokens |
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 "faile Inputs: job_id |
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 whi Inputs: bundle_id, limit, offset |
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 authe Inputs: receipt_id |
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 Inputs: url |
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.a Inputs: url, question, max_tokens |
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 ur Inputs: url, max_tokens |
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 inst Inputs: url, target_lang, max_tokens |
Every block below points at https://api.docimprint.com/mcp. Pick your client and copy.
claude mcp add --transport http docimprint-api https://api.docimprint.com/mcp
{
"mcpServers": {
"docimprint-api": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://api.docimprint.com/mcp"]
}
}
}{
"mcpServers": {
"docimprint-api": {
"url": "https://api.docimprint.com/mcp"
}
}
}{
"servers": {
"docimprint-api": {
"type": "http",
"url": "https://api.docimprint.com/mcp"
}
}
}{
"context_servers": {
"docimprint-api": {
"url": "https://api.docimprint.com/mcp"
}
}
}{
"mcpServers": {
"docimprint-api": {
"serverUrl": "https://api.docimprint.com/mcp"
}
}
}{
"mcpServers": {
"docimprint-api": {
"url": "https://api.docimprint.com/mcp"
}
}
}name: Docimprint API
version: 0.0.1
schema: v1
mcpServers:
- name: Docimprint API
type: streamable-http
url: https://api.docimprint.com/mcp{
"mcpServers": {
"docimprint-api": {
"httpUrl": "https://api.docimprint.com/mcp"
}
}
}{
"mcpServers": {
"docimprint-api": {
"type": "http",
"url": "https://api.docimprint.com/mcp"
}
}
}{
"mcpServers": {
"docimprint-api": {
"url": "https://api.docimprint.com/mcp"
}
}
}goose://extension?url=https%3A%2F%2Fapi.docimprint.com%2Fmcp&type=streamable_http&id=docimprint-api&name=Docimprint%20API
It answered our last check on 18 October 2026 in 359 ms. Over 10 checks since 4 September 2026 it answered 10. Status here comes from Neuronto's own probes, not from the publisher.
Yes. It is published by docimprint.com and served from api.docimprint.com, the vendor's own domain: https://api.docimprint.com/mcp.
22, as returned by the server's own tools/list on 10 September 2026: account.quota, bundle.get, bundle.notarize, bundle.verify, collection.add_document, collection.ask, collection.create, collection.list, and 14 more.
It answered our last check on 18 October 2026 in 359 ms. Over 10 checks since 4 September 2026 it answered 10.
Put the server under mcpServers in ~/.cursor/mcp.json with the URL https://api.docimprint.com/mcp. The exact JSON is above, with the same setup for Claude Code, Claude Desktop, VS Code, Windsurf, Zed, Gemini CLI and the rest.
It listed its tools to an anonymous client, so reading the tool list needs no key. Individual tools can still ask for credentials in their own inputs.
Publisher: com.docimprint. Endpoint host: api.docimprint.com. ARD identifier: urn:air:com.docimprint:mcp:api.
First indexed 31 August 2026. The tools are exactly what the server returned; nothing on this page
is inferred from its name. Ranking in Neuronto search is never for sale.