Algodocs

@Algodocs extract all invoice line items from the PDF I just uploaded to the 'Vendor Invoices' folder and return them as structured JSON

Extracted 12 line items from invoice #INV-2847. Each entry includes description, quantity, unit price, and total. Example: 'Cloud hosting services' (qty: 1, unit: $450.00, total: $450.00), 'API usage overage' (qty: 2500 calls, unit: $0.02, total: $50.00). Full structured data includes vendor name (Acme Cloud Inc.), invoice date (2024-01-15), due date (2024-02-14), subtotal ($1,240.00), tax ($99.20), and grand total ($1,339.20). This illustrative output shows how Algodocs parses document fields into machine-readable format.

Algodocs specializes in document data extraction via OCR and template matching. You must configure extraction templates in the Algodocs dashboard before prompts like this work — the MCP can't auto-detect arbitrary document structures. Accuracy depends on document quality and template training.

@Algodocs process the batch of 47 purchase orders in my 'Q1 POs' bucket and push the extracted data to our Google Sheet tracker

Batch processing initiated for 47 documents. Algodocs extracted vendor name, PO number, order date, line items, and total amount from each file. Successfully wrote 47 rows to the 'Q1 Purchase Orders' sheet (columns: PO Number, Vendor, Date, Item Count, Total). Processing took approximately 3 minutes. Three documents flagged for manual review due to low confidence scores on handwritten notes. This example demonstrates bulk document workflows with downstream integrations.

Batch operations require the target Google Sheet to be pre-configured in Algodocs integrations. The MCP triggers processing but doesn't create new sheets or columns — your template must map extracted fields to existing destinations. Rate limits apply to bulk jobs (typically 100 docs/hour on standard plans).

@Algodocs compare the extracted data from last month's expense reports and flag any receipts over $500 that are missing manager approval signatures

Analyzed 83 expense reports processed in December. Found 7 receipts exceeding $500: four airline tickets ($620-$890 each), two hotel stays ($545, $712), and one conference registration ($650). Cross-referenced extracted signature fields — 5 of these 7 lack a manager signature in the 'Approved By' region. Flagged documents: EXP-1204, EXP-1219, EXP-1223, EXP-1238, EXP-1241. This illustrative summary shows how Algodocs extraction enables compliance checks when paired with AI reasoning over structured data.

This example combines Algodocs' extraction with the AI's ability to filter and reason over the results. The MCP provides raw field data; the LLM applies your business logic (threshold checks, missing-field detection). Signature detection accuracy varies by document scan quality — always verify flagged items manually before enforcement.

When Algodocs makes sense for monthly close workflows

A 6-person agency gets 40-60 vendor invoices a month via email and needs line-item data in their accounting system by the 5th. Algodocs wins here if the invoices follow 3-5 consistent templates and the team wants to avoid manual keying. The MCP lets Switchy pull extraction results into a shared workspace where the bookkeeper reviews flagged items before export. This works because the volume is high enough to justify setup but low enough that API rate limits won't bite. If your invoices are one-off formats every time or you're processing thousands daily, the MCP becomes a bottleneck—Algodocs is built for repeatable document types at mid-scale. For teams closing books in under a week, this is the right call.

Using Algodocs to index signed agreements at startup scale

A 12-person SaaS company signs 15-20 customer contracts a quarter and needs renewal dates, payment terms, and liability caps searchable in one place. Algodocs handles this if the contracts share a standard template—most do after Series A when legal standardizes the paper. The MCP connects Switchy to the extraction API so the ops lead can review parsed fields in a shared canvas before pushing to the CRM. This scenario breaks down when contract language varies wildly (enterprise deals with heavy redlines) or when you need sub-clause analysis that requires an LLM, not just field extraction. If your contracts are 80% templated and you're tired of ctrl-F through PDFs, Algodocs gets you structured data without a paralegal.

When Algodocs fits early-stage recruiting workflows

A 10-person startup hiring for 3 roles gets 200 resumes in two weeks and wants skills, years of experience, and education in a spreadsheet for first-pass screening. Algodocs works if the resumes are mostly PDFs or Word docs in standard formats—LinkedIn exports, Canva templates, plain-text submissions. The MCP lets Switchy pull parsed candidate data into a hiring workspace where the founders tag must-interview profiles. This falls apart when resumes are image-heavy designer portfolios or when you need semantic matching beyond keyword extraction—that's LLM territory. If you're pre-ATS and doing resume triage in Notion or Airtable, Algodocs gives you structured rows without copy-paste hell.