Document Conversion API

Converts between PDF/Office documents and structured JSON, both directions. NEXUS candidate #6 -- manual build, not FORGE-generated, following the same manual-Cloud-Run-asset pattern as candidate #3 (agent-verification-api) and candidate #4 (url-metadata-api).

• POST /extract-pdf-to-json -- text, tables (per page), page count, metadata. $0.02/call.
• POST /extract-docx-to-json -- paragraphs (with heading levels/styles), tables, metadata. $0.01/call.
• POST /extract-xlsx-to-json -- per-sheet cell grids, capped rows/cols. $0.01/call.
• POST /generate-pdf-from-json -- structured blocks (heading/paragraph/table) -> PDF bytes. $0.02/call.
• POST /generate-docx-from-json -- structured blocks -> .docx bytes. $0.01/call.
• MCP tools at /mcp mirroring all 5 -- currently free, see "Known limitations".
• GET /health, GET /.well-known/agent-card.json, GET /openapi.json (has x-payment-info).

All 5 endpoints take base64-encoded file bytes directly in the request body -- never a URL to fetch. This is a deliberate scope boundary: it removes SSRF from this asset's risk surface entirely (unlike url-metadata-api/agent-verification-api, which both fetch caller-supplied URLs and need SSRF guards).

Why these libraries, why no external data source

Pure OSS, mature, no LLM calls, zero external network calls at runtime (fully local computation): pdfplumber (PDF extraction, wraps pdfminer.six), python-docx (Word), openpyxl (Excel), reportlab (PDF generation). No named third-party data source anywhere in this asset -- deliberately, to avoid the BuyWhere-style hallucination risk (skills/asset-lifecycle): there is nothing external to get wrong, the whole product is "run a well-known library over bytes the caller sent us." No numpy/scipy (avoids the known Cloud Run Buildpacks failure -- no Fortran compiler for scipy's source build -- see skills/infra-deploy-ops; none of these libraries need them anyway).

Why $0.01-$0.02 (vs $0.35 for agent-verification-api, $0.01 for url-metadata-api)

No paid third-party API cost here (unlike WHOIS in live-entity-verification/agent-verification-api) -- pure CPU/memory cost, so this sits in the same low tier as url-metadata-api's $0.01 single-fetch rather than anywhere near agent-verification-api's $0.35. PDF operations (extract-pdf-to-json, generate-pdf-from-json) are priced a notch higher ($0.02) than docx/xlsx ($0.01): pdfplumber/reportlab do meaningfully more work per call (page-level layout parsing / PDF rendering) than python-docx/openpyxl (straight XML parsing of a zip archive). This asset is expected to be the most predictable of the 3 manual candidates rather than the top earner -- document conversion is a common, low-variance agent need, not a differentiated/rare capability -- so pricing favors consistent low-friction usage over per-call margin.

Two risks this asset has that the other 3 manual assets don't

1. CPU-bound, not I/O-bound. Every parser/generator call is synchronous (no async API in any of the 4 libraries), unlike the other 3 assets which are I/O-bound via httpx.AsyncClient. Every handler offloads the actual work via asyncio.to_thread() inside an asyncio.wait_for() timeout (25s) so one caller's slow parse never blocks the whole event loop / every concurrent request. Known residual limitation: asyncio.wait_for() cancels the awaiting task, it cannot kill the underlying OS thread -- Python has no API to forcibly terminate a running thread. A pathological input that hangs pdfminer/openpyxl internally keeps that one worker thread occupied indefinitely even after the caller gets a 504. Mitigated, not solved: a bounded semaphore (NEXUS_MAX_CONCURRENT_JOBS, default 4) caps how many such "leaked" threads can accumulate concurrently -- new requests get a clean 503 instead of unbounded thread growth, but an already-leaked thread is never reclaimed. A full fix would need the CPU- bound work in a separate, killable process (ProcessPoolExecutor + hard terminate) rather than a thread; not done here as disproportionate for a 7-day probation candidate.
2. Decompression-bomb / resource-exhaustion. .docx/.xlsx are zip archives -- a crafted file with a small compressed size but a huge uncompressed size (zip bomb) is a real DoS vector distinct from anything the other 3 assets face. _check_zip_bomb_safe() inspects the zip central directory (zipfile.infolist(), cheap, does NOT decompress entry data) before handing bytes to python-docx/openpyxl: rejects if total uncompressed size would exceed 50MB, or if any single entry's compression ratio exceeds 100x. Every upload is also capped at 8MB raw/decoded bytes before any parsing at all (PDFs included, even though they aren't zip-based -- bounds worst-case input size regardless of format).

Known limitations (left unfixed on purpose -- CLAUDE.md §3, no gate without evidence it's needed)

• MCP tool calls are not charged. Same in-process-call pattern (and same reason) as the sibling manual assets: the MCP tool calls the shared conversion function directly, not via HTTP re-entry into the ASGI app.
• No per-caller rate limiting. Fine for a 7-day disposable measurement; add if it survives.
• Generated documents are not validated for round-trip fidelity beyond what's smoke-tested locally -- reportlab's table/paragraph rendering and python-docx's heading-level mapping are both mature, widely-used code paths, not re-verified against every possible Office/PDF reader here.
• See the "CPU-bound" risk above for the thread-leak residual limitation.

NEXUS_X402_FREE_MODE

Same gate pattern as similarity-search-api/live-entity-verification (skills/x402-payments) -- default false (charges from day 1, no freemium window; engine has no external validation dependency the way live-entity-verification's WHOIS-based engine did, so there's no equivalent "already proven in production" argument for skipping straight to paid -- it's charged anyway, per the session brief's "moderate and stable" revenue framing, not because of a specific precedent). Set true locally for testing without a real facilitator round-trip.

Deploy target: Cloud Run, not Railway

Same pipeline as candidates #3/#4 -- see skills/infra-deploy-ops. Memory bumped to 1Gi (vs. the shared scripts/deploy_cloud_run.sh's hardcoded 512Mi default) -- this is the heaviest-in-memory of the 3 manual candidates (PDF/Office parsing libraries, pypdfium2/Pillow transitively via pdfplumber). Deployed directly via gcloud run deploy (not through the shared script, to avoid editing shared infra tooling for a one-candidate memory bump):

# 1. First deploy -- PUBLIC_DOMAIN not known yet, every real request 421s until step 2.
gcloud run deploy document-conversion-api \
  --source manual_assets/document-conversion-api \
  --project nexus-505016 --region us-central1 \
  --allow-unauthenticated --min-instances=0 --max-instances=3 --memory=1Gi --quiet \
  --env-vars-file manual_assets/document-conversion-api/env-vars.deploy.yaml

# 2. Grab the printed *.run.app URL, then:
gcloud run services update document-conversion-api --region us-central1 --project nexus-505016 \
    --update-env-vars PUBLIC_DOMAIN=<the-real-domain>

Measurement (candidate #6, 7-day window)

7-day window from first real deploy. Source of truth: traffic_events/revenue_events/mcp_call_events tables (asset_name = 'document-conversion-api'), not Cloud Run logs. Day 7: if zero real traffic (filtering crawlers), pause/delete the Cloud Run service, same decision rule as candidates #3/#4.