westfarn
bb5aa6de82
## Summary - Add `WEBMCP_ENABLED` setting and guarded `webmcp-tools.js` loader on public pages - Register navigation tools (`list_services`, `get_page_content`, `navigate_to_service`, `open_contact_with_subject`) and contact tool (`submit_contact_inquiry`) - Annotate contact form with declarative WebMCP attributes for Lighthouse form coverage - Extend `PUBLIC_PAGE_ENTRIES` with summaries shared by SEO and WebMCP tools - Add `docs/webmcp.md`, update `docs/agentic-browsing.md`, and regression tests Closes #9 ## Test plan - [x] `python manage.py test public.tests` — 20 tests pass - [ ] Set `WEBMCP_ENABLED=True`, enable Chrome experimental web platform features flag - [ ] Verify homepage HTML includes `webmcp-config` and `webmcp-tools.js` - [ ] Verify `/contact` form has `toolname="submit_contact_inquiry"` - [ ] Run Lighthouse agentic-browsing on `/` and `/contact` with experimental flag - [ ] Call `list_services` from WebMCP-capable Chrome on homepage - [ ] Call `submit_contact_inquiry` on `/contact` with `DEBUG=True` (reCAPTCHA bypass) Reviewed-on: #12
165 lines
4.3 KiB
Python
165 lines
4.3 KiB
Python
"""Machine-readable site discovery endpoints for crawlers and AI agents."""
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from django.http import HttpResponse
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from django.template.loader import render_to_string
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from django.urls import reverse
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# Public marketing pages included in sitemap and llms.txt.
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# Tuple: (url_name, title, changefreq, priority, summary)
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PUBLIC_PAGE_ENTRIES = (
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(
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"public_index",
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"Home",
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"weekly",
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"1.0",
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"Company homepage with an overview of AI ML Operations services.",
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),
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(
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"forward_deployed",
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"Forward-Deployed AI",
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"monthly",
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"0.9",
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"Embedded AI engineering — we work inside your environment to build production systems.",
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),
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(
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"bot",
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"AI Agents",
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"monthly",
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"0.9",
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"Custom AI agents and agentic workflows tailored to your operational bottlenecks.",
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),
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(
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"ml_model",
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"ML Models",
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"monthly",
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"0.8",
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"Machine learning model development, training, and deployment for production use.",
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),
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(
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"chat",
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"Secure AI Chat",
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"monthly",
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"0.8",
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"Private, hosted AI chat deployments with enterprise-grade security.",
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),
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(
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"ai_sensor",
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"AI Sensor Algorithms",
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"monthly",
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"0.7",
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"Computer vision and sensor-fusion algorithms for real-world sensing applications.",
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),
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(
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"ai_education",
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"AI Education",
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"monthly",
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"0.7",
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"Hands-on AI training and workshops for teams adopting agentic workflows.",
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),
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(
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"computers",
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"Computer Builds",
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"monthly",
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"0.7",
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"Custom workstation and server builds optimized for AI and ML workloads.",
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),
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(
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"file_hosting",
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"File Hosting",
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"monthly",
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"0.7",
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"Managed file hosting and storage for teams that need reliable data access.",
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),
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(
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"web_design",
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"Web Design and Hosting",
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"monthly",
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"0.8",
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"Web design, development, and managed hosting for business sites and apps.",
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),
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(
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"contact",
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"Contact",
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"monthly",
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"0.9",
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"Contact form to inquire about forward-deployed AI engineering services.",
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),
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(
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"terms_of_service",
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"Terms of Service and Privacy",
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"yearly",
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"0.3",
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"Terms of service and privacy policy for AI ML Operations, LLC.",
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),
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)
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SERVICE_URL_NAMES = frozenset({
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"forward_deployed",
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"bot",
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"ml_model",
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"chat",
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"ai_sensor",
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"ai_education",
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"computers",
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"file_hosting",
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"web_design",
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})
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def get_service_entries():
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"""Return service page metadata for WebMCP navigation tools."""
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return [
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{
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"slug": url_name,
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"name": title,
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"summary": summary,
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}
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for url_name, title, _changefreq, _priority, summary in PUBLIC_PAGE_ENTRIES
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if url_name in SERVICE_URL_NAMES
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]
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def _absolute_url(request, url_name):
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return request.build_absolute_uri(reverse(url_name))
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def robots_txt(request):
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sitemap_url = _absolute_url(request, "sitemap_xml")
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content = render_to_string(
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"public/robots.txt",
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{"sitemap_url": sitemap_url},
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)
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return HttpResponse(content, content_type="text/plain; charset=utf-8")
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def sitemap_xml(request):
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pages = [
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{
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"loc": _absolute_url(request, url_name),
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"changefreq": changefreq,
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"priority": priority,
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}
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for url_name, _title, changefreq, priority, _summary in PUBLIC_PAGE_ENTRIES
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]
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content = render_to_string("public/sitemap.xml", {"pages": pages})
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return HttpResponse(content, content_type="application/xml; charset=utf-8")
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def llms_txt(request):
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pages = [
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{
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"title": title,
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"url": _absolute_url(request, url_name),
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}
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for url_name, title, _changefreq, _priority, _summary in PUBLIC_PAGE_ENTRIES
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]
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content = render_to_string(
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"public/llms.txt",
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{
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"site_url": request.build_absolute_uri("/"),
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"contact_url": _absolute_url(request, "contact"),
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"pages": pages,
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},
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)
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return HttpResponse(content, content_type="text/plain; charset=utf-8")
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