PDF Vector

@PDF-Vector parse this invoice PDF and return line items, totals, and vendor details as structured JSON.

Parsed invoice #INV-2024-0847 from Acme Supplies dated March 15, 2024. Extracted 12 line items totaling $3,847.50 before tax. Vendor: Acme Supplies Inc., 450 Commerce St, Austin TX. Payment terms: Net 30. This extraction used the base model and consumed 2.3 credits. The structured output includes item descriptions, quantities, unit prices, and tax breakdown ready for import into your accounting system.

This example shows schema-constrained extraction from invoices. The MCP consumes credits per document and reports the cost in the response. You must provide an API key with sufficient credit balance. The tool will not automatically retry with a more expensive model if extraction fails—you control cost vs. accuracy trade-offs explicitly.

@PDF-Vector what are the key findings and methodology from this research paper? https://arxiv.org/pdf/2401.12345

Parsed arXiv:2401.12345 to markdown (4.1 credits). Key findings: The authors demonstrate a 23% improvement in zero-shot classification accuracy using contrastive pre-training on domain-specific corpora. Methodology: They fine-tuned a transformer encoder on 50K labeled examples from three verticals, then evaluated on held-out test sets. Limitations noted include sensitivity to hyperparameter selection and computational cost during pre-training. The full markdown is available for deeper analysis.

This example demonstrates academic paper parsing from a public PDF URL. The MCP resolves the identifier, downloads the PDF, and converts it to markdown while tracking credit usage. Useful for literature reviews or citation workflows. Note that only publicly accessible PDFs can be parsed—paywalled papers will fail unless you have institutional access configured.

@PDF-Vector find 5 papers similar to doi:10.1038/s41586-023-06291-2 and show how they cite each other.

Found 5 papers related to the Nature study on protein folding via citation-network similarity. Top result: 'AlphaFold2 applications in drug discovery' (cited 340 times, cites the seed paper). Second: 'Comparative analysis of structure prediction tools' (mutual citation with seed). Third through fifth cover experimental validation, computational efficiency, and clinical applications. The citation graph shows the seed paper is a hub—4 of 5 results cite it directly, and 2 form a citation cluster among themselves.

This example highlights citation-network traversal and relationship mapping. The MCP uses provider APIs (Semantic Scholar, CrossRef) to resolve identifiers and fetch citation data. Relationship edges help you understand influence and research lineage. Be aware that citation counts and graph completeness depend on the upstream provider's coverage—recent preprints may have sparse metadata.

When PDF Vector beats manual invoice keying for small shops

A 3-person accounting firm processing 40-80 client invoices per month hits the sweet spot for PDF Vector's Extract Document Data tool. The MCP reads invoice PDFs, pulls vendor name, line items, totals, and dates into a schema you define, then hands back JSON your bookkeeping software can ingest. You skip the double-entry risk and save 15 minutes per invoice. The credit-per-document model makes sense at this volume; if you're doing 500+ invoices monthly, a dedicated OCR pipeline with volume pricing will cost less. Auth is straightforward API key setup. If your invoices are mostly the same 10 vendors, PDF Vector learns the layouts fast and accuracy stays above 95%. Buy this if you're drowning in PDFs but not yet big enough to justify a full document-automation stack.

How PDF Vector accelerates academic citation discovery

A university research team writing an NIH grant needs 40-60 citations spanning three related subfields. PDF Vector's Find Citations and Find Similar Papers tools let you seed the search with 2-3 anchor papers, then walk the citation graph to surface relevant work you missed. The Parse Academic Paper tool converts paywalled PDFs to markdown so you can quote methods sections without re-typing. This workflow cuts lit-review time from two weeks to three days for a typical R01. The catch: if your field is niche or pre-2010, citation coverage thins out and you'll still need manual PubMed passes. The Get Paper Graph tool shows citing and referenced papers in one call, which helps you spot review articles that summarize a subfield. Buy this if your team writes grants quarterly and citation accuracy matters more than speed.

When PDF Vector turns product manuals into searchable answers

A 6-person support team at a B2B SaaS company fields 200 tickets per week, half of which reference a 300-page PDF product manual. PDF Vector's Ask Document tool lets agents query the manual in natural language and get paragraph-length answers with page citations. You upload the manual once, then agents ask 'how do I configure SSO for Azure AD' and get the exact steps without scrolling. This works when your manual is well-structured and under 500 pages; larger docs hit token limits and answers get vague. The Parse Document tool converts the PDF to markdown first, which improves search accuracy by 20% over raw text extraction. Buy this if your support load is growing but you're not ready to build a full RAG pipeline with Pinecone or Weaviate.