OkraPDF Public Documents
Pre-extracted public document corpora queryable via MCP. No upload, no waiting — documents are already parsed and indexed. Just pass an ID and start asking questions.
Available Channels
| Channel | Coverage | Auth | ID Format |
|---|
| Arxiv AI papers | 400+ papers from cs.AI, cs.CL, cs.LG (updated weekly) | API key required | arxiv:2603.26653 |
| SEC filings | Mag7 + FinanceBench (~80 companies), 10-K and 10-Q | No auth needed | Ticker-based (NVDA) |
Setup
Arxiv papers (authenticated MCP)
Add to ~/.claude/mcp.json (Claude Code) or .cursor/mcp.json (Cursor):
{
"mcpServers": {
"okra-pdf": {
"type": "url",
"url": "https://api.okrapdf.com/mcp",
"headers": { "Authorization": "Bearer YOUR_API_KEY" }
}
}
}
Get a free API key at okrapdf.com (Settings > API Keys).
SEC filings (zero-auth MCP)
{
"mcpServers": {
"okra-sec": {
"type": "url",
"url": "https://mcp.okrapdf.com/mcp"
}
}
}
No API key, no signup. Restart your agent after adding.
Arxiv Papers
400+ recent AI research papers parsed with Docling OCR on GPU — tables, equations, figures, and full text preserved as structured markdown.
Read a paper
read_document(document_id: "arxiv:2603.26653")
read_document(document_id: "arxiv:2603.26653", pages: "1-5")
read_document(document_id: "https://arxiv.org/pdf/2603.26653")
No upload needed — papers are pre-indexed as public sources. Just pass the arxiv ID.
Ask questions
ask_document(document_id: "arxiv:2603.26653", question: "What is the main contribution?")
ask_document(document_id: "arxiv:2603.26653", question: "What were the benchmark results on MMLU?")
ask_document(document_id: "arxiv:2603.18272", question: "How is retrieval-augmented experience used?")
Returns answer with page citations.
Extract structured data
extract_data(
document_id: "arxiv:2603.26653",
prompt: "Extract all benchmark results with model names, dataset names, and scores",
json_schema: {
"type": "object",
"properties": {
"benchmarks": {
"type": "array",
"items": {
"type": "object",
"properties": {
"model": {"type": "string"},
"dataset": {"type": "string"},
"metric": {"type": "string"},
"score": {"type": "number"}
}
}
}
}
}
)
Literature survey workflow
# Read abstracts from several papers
read_document(document_id: "arxiv:2603.26499", pages: "1")
read_document(document_id: "arxiv:2603.26266", pages: "1")
# Ask targeted questions
ask_document(document_id: "arxiv:2603.26499", question: "What bottlenecks in AI research does this address?")
# Same question across papers for comparison
ask_document(document_id: "arxiv:2603.18272", question: "How does this handle multi-agent coordination?")
ask_document(document_id: "arxiv:2603.07379", question: "How does this handle multi-agent coordination?")
Discover papers
Semantic Scholar (free, no key needed for basic use):
curl -s "https://api.semanticscholar.org/graph/v1/paper/search?query=agentic+RAG&year=2026&fields=externalIds,title,citationCount&limit=10" \
| jq '.data[] | {arxiv: .externalIds.ArXiv, title, citations: .citationCount}'
Arxiv RSS feeds (same feeds used to build the collection):
https://rss.arxiv.org/rss/cs.AI # Artificial Intelligence
https://rss.arxiv.org/rss/cs.CL # Computation and Language (NLP)
https://rss.arxiv.org/rss/cs.LG # Machine Learning
Papers With Code:
curl -s "https://paperswithcode.com/api/v1/papers/?q=agentic+RAG&items_per_page=5" | jq '.results[] | {title, arxiv_id}'
Current snapshot
411 papers from cs.AI (~200), cs.CL (~100), cs.LG (~200). Full manifest in papers.json.
If a paper isn't found, upload it yourself with upload_document.
Tips
- Use
arxiv:XXXX.XXXXX format (not full URL) for cleaner queries
pages: "1" reads just the abstract/intro quickly- For survey papers (50+ pages), use
ask_document instead of reading everything
extract_data with JSON schemas is ideal for pulling benchmark tables
SEC Filings
Pre-extracted SEC 10-K and 10-Q filings. No API key, no signup, completely free.
Available tools
| Tool | Purpose |
|---|
read_filing_index | Browse available filings, filter by ticker/type |
read_filing_contents | Get full extracted text as markdown |
ask_question | AI-powered Q&A with citations, single or cross-company |
get_verification_summary | Check extraction quality page-by-page |
verify_pages | Approve or flag pages for quality control |
Browse filings
read_filing_index()
read_filing_index(ticker: "NVDA")
read_filing_index(ticker: "AAPL", filing_type: "10-K")
Always start here to see what's available.
Ask questions (single company)
ask_question(question: "What was NVIDIA's data center revenue?", tickers: ["NVDA"])
ask_question(question: "List all risk factors related to AI regulation", tickers: ["MSFT"])
ask_question(question: "What are the outstanding debt obligations?", tickers: ["TSLA"], filing: "10-k-2024")
Cross-company comparison (up to 10 tickers)
ask_question(
question: "Compare R&D spending as a percentage of revenue",
tickers: ["AAPL", "MSFT", "GOOGL", "NVDA", "META", "AMZN", "TSLA"]
)
ask_question(
question: "Which company has the highest gross margin?",
tickers: ["AAPL", "MSFT", "GOOGL"]
)
ask_question(
question: "Summarize each company's AI strategy",
tickers: ["NVDA", "AMD", "INTC"]
)
Fans out to each company's filing in parallel, then synthesizes a cross-company answer.
Read full filing text
read_filing_contents(ticker: "TSLA", filing: "10-k-2024")
Filing slug formats (all equivalent): 10-k-2024, 10-K/2024, 2024-10K.
Extraction quality audit
get_verification_summary(document_id: "doc-xxx")
get_verification_summary(document_id: "doc-xxx", status: "needs_review")
verify_pages(document_id: "doc-xxx", action: "approve", confidence_above: 0.9)
verify_pages(document_id: "doc-xxx", action: "flag", pages: [67], reason: "Table has merged cells")
Available companies
Mag7: AAPL, MSFT, GOOGL, AMZN, NVDA, META, TSLA
FinanceBench: ~80 companies including major banks (JPM, BAC, GS), pharma (PFE, JNJ, ABBV), industrials (GE, MMM, CAT), and more.
Use read_filing_index() to browse the full catalog. New filings added as published.
Tips
- Start with
read_filing_index before querying
ask_question with multiple tickers is the fastest way to compare — no need to read each filing- Cross-company queries work best with clear, quantitative questions
- Verification tools require
document_id (not ticker) — get it from other tool responses
