[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_109":110},"routes/modules.$creator.$moduleId",{"_14":15,"_16":17,"_18":19,"_20":21},"routes/modules.$creator.$moduleId.endpoints",{"_11":12,"_13":-5},"error","Module not found","tools","module",{"_20":22,"_23":24,"_25":26,"_27":28,"_29":30,"_31":32,"_33":34,"_35":26,"_36":37,"_38":-5,"_39":-5,"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55,"_56":-5,"_57":49,"_58":59,"_60":61,"_62":63,"_64":65,"_66":67,"_68":69,"_70":71,"_72":73,"_74":75,"_76":77,"_78":79,"_80":-5,"_81":-5,"_82":-5,"_83":49,"_84":-5,"_85":59,"_86":87,"_88":89,"_90":91,"_92":93,"_94":95},"apiUrl","https://odel.app/api","creator","manvigallc007-spec","moduleId","indousmcp","@manvigallc007-spec/indousmcp","displayName","Namaste America","tagline","Agent-first directory for Indians from India in the USA — restaurants, temples, events & more.","iconKey","https://avatars.githubusercontent.com/u/269627817?v=4","category","remote","status","published","visibility","public","metaDescription","deploymentType","external","workerName","bindingName","mcpConfig","{\"url\":\"https://namasteamerica.us/mcp\",\"transport\":\"streamable-http\",\"authType\":\"none\"}","pricingModel","free","creatorUserId","system:external-import","createdAt",1783571584000,"updatedAt",1788333985000,"sourceRegistry","official-mcp-registry","sourceUrl","https://registry.modelcontextprotocol.io/v0/servers?search=us.namasteamerica%2Fnamaste-america","sourceVerified",false,"sourceMissingSince","importedAt","upstreamStars",0,"upstreamUpdatedAt",1784805345000,"detectedTransport","streamable-http","detectedAuthType","none","probeStatus","ok","lastProbedAt",1787637365000,"githubRepoUrl","https://github.com/manvigallc007-spec/indousmcp","githubRepoFullName","manvigallc007-spec/indousmcp","releaseId","ext-manvigallc007-spec-indousmcp","version","0.1.0","descriptionMarkdown","# Indian–American Diaspora MCP Server\n\nAgent-first, agent-only data layer for the Indian-American diaspora. **Phase 1: Indian\nrestaurants (USA).** This repo is the walking skeleton from the architecture blueprint:\n\n- **MCP server** exposing restaurant capabilities (`get_indian_restaurants`,\n `get_restaurant_details`, `search_restaurants_by_text`, `find_unclaimed_restaurants`,\n `draft_claim_outreach`, `submit_correction`), **temple** capabilities\n (`get_indian_temples`, `get_temple_details`, `search_temples_by_text`) and **grocery**\n capabilities (`get_indian_groceries`, `get_grocery_details`, `search_groceries_by_text`)\n **professional** capabilities (`get_indian_professionals`, …), **salon** capabilities\n (`get_indian_salons`, …), and a cross-vertical **`search_all`** that searches every vertical\n at once (each result tagged with its `vertical`).\n- **Data pipeline**: scrape → raw → clean/enrich/score → approval queue → canonical table →\n versioning.\n- **One real scraper**: OpenStreetMap Overpass (public, ODbL-licensed, no login, ToS-safe).\n- **Storage**: PostgreSQL (+ `pgvector` for future embedding search) via Docker Compose.\n\n## Connect (live MCP server)\n\nThe hosted server is **remote** (no install) over streamable‑HTTP:\n\n- **Endpoint:** `https://namasteamerica.us/mcp` (transport: `streamable-http`, auth: none, read‑only)\n- **Descriptor:** `https://namasteamerica.us/.well-known/mcp.json` · **Docs:** `https://namasteamerica.us/for-agents`\n\n```json\n{ \"mcpServers\": { \"namaste-america\": { \"type\": \"streamable-http\", \"url\": \"https://namasteamerica.us/mcp\" } } }\n```\n\n**Tools** (per vertical: restaurants, temples, groceries, professionals, salons, events, apparel,\nsweets, studios, services, community, legal, education, real estate, finance):\n`get_indian_`, `search__by_text`, `get__details`, and a cross‑vertical\n**`search_all`**. Each result is JSON with address, geo, hours, contact, ratings, and languages.\n\n> Publishing to the official MCP registry: the manifest is [`server.json`](https://github.com/manvigallc007-spec/indousmcp/blob/main/server.json) (namespace\n> `us.namasteamerica`, verified via DNS). Install the CLI (`mcp-publisher`), authenticate the domain\n> (`mcp-publisher login dns --domain namasteamerica.us`, then add the TXT record it prints), and run\n> `mcp-publisher publish`.\n\n## Architecture (Phase 1)\n\n```\n OSM Overpass ──▶ restaurant_raw ──▶ clean()+score() ──▶ approval_queue ──▶ restaurants\n (scraper) (JSONB) (pipeline) (high-risk only) (canonical)\n │\n ▼\n restaurant_versions\n │\n ▼\n MCP tools (FastMCP)\n```\n\nLow-risk new inserts are auto-applied (configurable); updates to claimed/featured listings\nare routed to the human approval queue.\n\n## Quick start\n\nRequires Docker Desktop and Python 3.11+.\n\n```powershell\n# 1. Start Postgres (with pgvector)\ndocker compose up -d\n\n# 2. Create a virtualenv and install\npython -m venv .venv\n.\\.venv\\Scripts\\Activate.ps1\npip install -e .\n\n# 3. Configure env\ncopy .env.example .env # default DATABASE_URL already points at the docker container\n\n# 4. Initialise the schema\npython -m indo_usa_mcp.cli init-db\n\n# 5. Scrape one metro from OpenStreetMap, then process raw -> canonical\npython -m indo_usa_mcp.cli scrape --metro bay_area\npython -m indo_usa_mcp.cli process\n\n# 6. Inspect\npython -m indo_usa_mcp.cli stats\n\n# 7. Run the MCP server (stdio transport)\npython -m indo_usa_mcp.server\n```\n\n## CLI\n\n| Command | Purpose |\n|---|---|\n| `init-db` | Apply SQL migrations (extensions, tables, indexes). |\n| `scrape --metro ` | Run the OSM scraper for a metro bbox into `restaurant_raw`. |\n| `process` | Clean/score unprocessed raw rows; auto-apply low-risk, queue high-risk. |\n| `approvals` | List pending approval-queue items. |\n| `approve ` / `reject ` | Resolve an approval item. |\n| `outreach [--limit N]` | Draft claim outreach for unclaimed restaurants (creates claim links + messages). |\n| `verify-claim ` | Owner-side: verify a claim token and take ownership. |\n| `agents` | List the registered autonomous agents. |\n| `agent ` | Run one agent now (audited in `agent_runs`). |\n| `agents-loop [--once]` | Run the scheduler (worker loop over due agents). |\n| `query [--city/--text/--lat --lng/--id/...]` | Call the MCP tool functions from the terminal. |\n| `seed` | Load fictional seed restaurants for local testing (no scrape needed). |\n| `enrich` | Backfill region/dietary cultural tags on under-tagged restaurants. |\n| `deactivate-stale [--days 60]` | Mark unclaimed listings not seen recently as inactive. |\n| `approval-digest` | Human-readable summary of the pending approval queue. |\n| `feedback --id N --field F --value V` | Submit a field correction (applied by the feedback agent). |\n| `scrape --metro usa` | Nationwide sweep (occasional; slower than a single metro). |\n| `feature --id N [--days 30 \\| --permanent]` | Mark a paid featured listing. |\n| `unfeature --id N` | Remove a featured listing. |\n| `backfill-embeddings [--all]` | (Re)compute embeddings for canonical rows. |\n| `stats` | Row counts and coverage summary. |\n\n## Monetization & delivery\n\n- **Featured listings** (`feature`/`unfeature`) — a paid tier; effectively-featured rows\n (flagged and within their `featured_until` window) surface first in every tool result.\n- **Stripe checkout** (optional) — claimed owners get a \"Get Featured\" button → Stripe\n Checkout → a signature-verified webhook (`/stripe/webhook`) auto-features them. Disabled\n until `STRIPE_SECRET_KEY` is set (then it's pay-per-sale, no monthly fee). See DEPLOY.md.\n- **Outreach email delivery** — optional and zero-cost: set Gmail SMTP + an app password in\n `.env` (see `.env.example`) and the Outreach Agent will auto-send claim emails to\n restaurants that have a public email; otherwise it stays draft-only.\n- **Enrichment agent** — strengthens the cultural \"data moat\" by inferring region/dietary\n tags from restaurant names (free, keyword-based).\n\nCoverage spans 15 metros (Bay Area, NYC/NJ, Dallas, Houston, Chicago, LA, Seattle, Atlanta,\nPhoenix, Austin, Boston, Philadelphia, Raleigh, Detroit, Central NJ) plus an on-demand\n**nationwide** sweep (`scrape --metro usa`).\n\nWhatsApp outreach is delivered as free **click-to-send `wa.me` links** (message pre-filled);\ntrue auto-send WhatsApp needs a paid API and is intentionally not used.\n\n## Trying it without a live scrape\n\n```powershell\npython -m indo_usa_mcp.cli init-db\npython -m indo_usa_mcp.cli seed # 12 fictional restaurants across the 5 metros\npython -m indo_usa_mcp.cli stats\n```\n\nThen exercise the MCP tools (e.g. `get_indian_restaurants` near the Bay Area, or\n`search_restaurants_by_text \"vegetarian dosa\"`).\n\n## Search quality (for LLM agents)\n\nRecords are optimized for agent retrieval:\n- **Natural-language `description`** per record (e.g. *\"Saffron House is a Gujarati Indian\n restaurant in Edison, NJ. Offers vegetarian, jain options. Price: $$.\"*) — generated from\n the structured fields, returned to agents, and used as the text that gets embedded.\n- **Reverse-geocoded location** — missing `city`/`state` are filled offline from coordinates\n (`reverse_geocoder`), so \"near me / in \" queries work for every record.\n- **Keyword + attribute `tags`** (e.g. `biryani`, `dosa`, `halal`, `delivery`, `takeout`,\n `outdoor-seating`, `wheelchair-accessible`, `wifi`, `cards-accepted`, `organic`) — derived\n from names and rich OSM attributes; agents filter with `tag=...`, and they boost embedding\n recall + appear in the description (\"Amenities: …\").\n- **`open_now`** — `opening_hours` are parsed into structured per-day intervals; each result\n carries an `open_now` flag (true/false/null) and the tools accept an `open_now=true` filter,\n enabling \"what's open near me right now\".\n- **Dedup-merge** — the admin Quality view lists duplicate groups with a one-click merge\n (fills the keeper's empty fields, soft-deletes the rest).\n\n`search_*_by_text` ranks by embedding cosine distance (pgvector `<=>`), falling back to\ntrigram. Providers (`EMBEDDING_PROVIDER`):\n\n- **`hashing`** (default) — feature-hashing, zero extra deps; lexical.\n- **`fastembed`** (recommended, **prod default**) — real semantic embeddings via\n `BAAI/bge-small-en-v1.5` (384-dim, ONNX, **no torch**). The Docker image includes it and the\n prod compose defaults `EMBEDDING_PROVIDER=fastembed`; after deploying run\n `python -m indo_usa_mcp.cli enhance-data` once to re-embed existing rows. Set\n `EMBEDDING_PROVIDER=hashing` if the VPS is RAM-constrained.\n- **`sentence_transformers`** — `all-MiniLM-L6-v2` (heavier, pulls torch).\n- **`none`** — trigram only.\n\n`enhance-data` (re)generates descriptions, fills geocoding, and re-embeds existing rows —\nrun it after enabling a new embedder. New records get all of this automatically on ingest.\n\n## Outreach & claiming (blueprint §7)\n\n```powershell\n# Draft claim messages + single-use claim links for unclaimed restaurants\npython -m indo_usa_mcp.cli outreach --limit 10\n\n# Later, an owner verifies their claim token (normally via the claim web page)\npython -m indo_usa_mcp.cli verify-claim --email owner@example.com\n```\n\nAfter claiming, owners get an **edit page** (`/manage?token=...`) to update their phone,\nhours, menu, price, dietary tags, etc. — changes go live immediately and are protected from\nscraper overwrites (scraper updates to claimed listings route to the approval queue).\n**Stale listings** not re-seen for 60 days are auto-deactivated (`is_active=false`) and\nreactivated if they reappear in a later scrape.\n\nThe Outreach Agent finds unclaimed restaurants (skipping anything with an open claim or\ncontacted within `OUTREACH_COOLDOWN_DAYS`), creates a single-use claim token + link, and\ndrafts an honest, opt-out-friendly message per restaurant. Messages are **not auto-sent** —\ndelivery needs channel integrations, and chains / featured / high-value targets are flagged\n`requires_human`. Once an owner verifies, `restaurants.is_claimed` flips to true and future\nscraper updates to that listing are routed to the approval queue. Agents can drive this via\nthe `find_unclaimed_restaurants` and `draft_claim_outreach` MCP tools.\n\n## Autonomous agents (blueprint §6)\n\nEach agent wraps a pipeline step, is idempotent, and writes a full audit row to\n`agent_runs` (errors captured, never half-written canonical data).\n\n| Agent | Does |\n|---|---|\n| `discovery` | Reports metro coverage and proposes scrape targets. |\n| `scraper` | Runs every scraper across every metro into `restaurant_raw`. |\n| `cleaner` | Processes raw → canonical via clean/score/approval. |\n| `outreach` | Drafts claim outreach for eligible unclaimed restaurants. |\n| `monitoring` | Detects anomalies (backlogs, scraper failures, stale data) → `agent_alerts`. |\n| `submission` | Submits the MCP to directories (manual stub for now). |\n\n```powershell\npython -m indo_usa_mcp.cli agents # list agents\npython -m indo_usa_mcp.cli agent scraper # run one, audited\npython -m indo_usa_mcp.cli agents-loop --once # one scheduler pass (scrape→clean→monitor…)\npython -m indo_usa_mcp.agents.scheduler # long-lived scheduler (VPS worker)\n```\n\n### Data sources\n\nTwo independent public scrapers feed the pipeline: `osm_overpass` (OpenStreetMap, ODbL)\nand `wikidata` (Wikidata SPARQL, CC0). Pick one with `scrape --source `; the\nScraper Agent runs both.\n\n## Phase 2: Temples vertical\n\nAn independent vertical (Hindu/Sikh/Jain places of worship) sharing the same pipeline,\nagents, embeddings and deployment — per the blueprint's \"independent verticals, shared\ninfra\" principle. Own table (`temples`), own OSM scraper (`amenity=place_of_worship` +\n`religion`), own agents (`temple_scraper`, `temple_cleaner`), own MCP tools. Cultural\nenrichment infers **deity** (Venkateswara, Krishna, Lakshmi…) and **region** (Punjabi for\nSikh, Gujarati for Swaminarayan, Telugu for Venkateswara…) from the name.\n\n```powershell\npython -m indo_usa_mcp.cli temples-scrape --metro bay_area\npython -m indo_usa_mcp.cli temples-process\npython -m indo_usa_mcp.cli temples-stats\npython -m indo_usa_mcp.cli temples-query --religion hindu --city Fremont\npython -m indo_usa_mcp.cli temples-query --text \"swaminarayan gujarati mandir\"\n```\n\n## Phase 2: Groceries vertical\n\nSame recipe as temples — independent `groceries` table + OSM scraper (grocery-type shops\nwhose name signals Indian groceries: Patel, India, Apna, Swad, Masala…) + own agents\n(`grocery_scraper`, `grocery_cleaner`) + 3 MCP tools. Infers `store_type` and `region_tag`.\n\n```powershell\npython -m indo_usa_mcp.cli groceries-scrape --metro bay_area\npython -m indo_usa_mcp.cli groceries-process\npython -m indo_usa_mcp.cli groceries-query --city Fremont\npython -m indo_usa_mcp.cli groceries-query --text \"patel brothers indian grocery\"\n```\n\n## Admin dashboard, reporting & owner portal\n\nThe web app (`:8080`) now serves three audiences:\n\n- **Public/owner** — `/`, `/claim`, `/manage`, `/upgrade` (existing)\n- **Admin** (`/admin/*`, password-gated) — overview KPIs, cross-vertical **data control**\n (browse/search/edit/feature/deactivate/soft-delete), **Geography** drill-down\n (country → state → city, click to filter records), **Quality** control (records flagged for\n missing region/contact/geo/city + duplicate groups, with a one-click city/state normalizer),\n **approvals** & **feedback** queues, **agents** (last-run health, run-now, resolve alerts),\n **Traffic** (every MCP tool call logged: by tool / agent-client / day, plus **most-shown\n listings**), **payments**, and **reports**. Set `ADMIN_PASSWORD` to enable; blank disables it.\n- Owners see a **reach** number per listing in the portal (\"shown N times by AI assistants\",\n from per-listing impression tracking) — a concrete hook for selling featured placement.\n- **Customer portal** (`/portal/*`) — owners sign in via **passwordless magic-link** email and\n manage all their listings across verticals (edit, featured status, upgrade).\n\n**Daily report**: the `reporting` agent computes a health + growth snapshot nightly into\n`daily_reports`, shows it on `/admin/reports`, and emails it (via SMTP) to `REPORT_EMAIL`.\nRun on demand with `python -m indo_usa_mcp.cli report`.\n\n> Security: enable HTTPS (the Caddy `tls` profile) and set a strong `ADMIN_PASSWORD` +\n> random `SECRET_KEY` before exposing `/admin` publicly.\n\n## Phase 2: Professionals vertical (doctors/health)\n\nIndian-American healthcare professionals — doctors, dentists, clinics, pharmacies — found\nfrom OSM healthcare amenities + an Indian-name signal (surnames / Ayurveda). Same recipe as\nthe other verticals: own `professionals` table, scraper, agents (`professional_scraper`,\n`professional_cleaner`), 3 MCP tools, plus `profession_type`/`speciality` filters. Because\nname-matching is heuristic, each record carries a `confidence_score` and the admin Quality\nview helps curate false positives.\n\n```powershell\npython -m indo_usa_mcp.cli professionals-scrape --metro bay_area\npython -m indo_usa_mcp.cli professionals-process\npython -m indo_usa_mcp.cli professionals-query --type dentist --city \"San Jose\"\n```\n\n## Phase 2: Salons vertical (beauty / threading)\n\nIndian beauty salons — eyebrow threading, henna/mehndi, hair, bridal — from OSM\nhairdresser/beauty shops with a strong South-Asian service/name signal. Same recipe; tags\ncapture services (threading, henna, bridal…). Best OSM coverage of the verticals so far.\n\n```powershell\npython -m indo_usa_mcp.cli salons-scrape --metro bay_area\npython -m indo_usa_mcp.cli salons-process\npython -m indo_usa_mcp.cli salons-query --tag threading --city Sunnyvale\n```\n\n## Phase 2: Events vertical (automated, admin-approved)\n\nIndian-American community events (festivals, garba, concerts, puja). **Fully automated** —\nan agent **auto-discovers** iCal feeds by scanning the websites of orgs already in the\ndatabase (temples first) for calendar links, and agents ingest events from those feeds plus\nany in `EVENT_ICAL_FEEDS`. The\ncleaner **auto-approves high-confidence events and queues the rest** under *Admin → Events*\nfor one-click approval (no manual entry). **Past events are kept, not deleted** — they're\ndate-filtered out of upcoming results (`get_indian_events` returns upcoming by default;\n`include_past=true` for history) and an 18-month retention purges very old ones.\n\n```powershell\npython -m indo_usa_mcp.cli events-discover # scan org websites for iCal feeds\npython -m indo_usa_mcp.cli events-scrape # ingest configured + discovered iCal feeds\npython -m indo_usa_mcp.cli events-process # auto-approve / queue for admin\npython -m indo_usa_mcp.cli events-approvals # list pending\npython -m indo_usa_mcp.cli events-query --city Edison --category garba\n```\n\n## Connecting an MCP client\n\nPoint any MCP client (Claude Desktop, etc.) at:\n\n```json\n{\n \"mcpServers\": {\n \"indo-usa-diaspora\": {\n \"command\": \"python\",\n \"args\": [\"-m\", \"indo_usa_mcp.server\"],\n \"env\": { \"DATABASE_URL\": \"postgresql://diaspora:diaspora@localhost:5433/diaspora\" }\n }\n }\n}\n```\n\n## Deployment (VPS, blueprint §10)\n\nThe full hosted stack — Postgres + MCP server (HTTP) + agent worker — is in\n[docker-compose.prod.yml](https://github.com/manvigallc007-spec/indousmcp/blob/main/docker-compose.prod.yml):\n\n```powershell\n$env:POSTGRES_PASSWORD = \"choose-a-strong-password\"\ndocker compose -f docker-compose.prod.yml up -d --build\n```\n\n- **server** runs migrations (idempotent) then serves MCP over `streamable-http` on `:8000`.\n- **worker** runs the agent scheduler (scrape → clean → monitor …); it waits for the schema\n before starting, so service start order doesn't matter.\n- **db** stays on the internal network (not published).\n\nPut a TLS-terminating reverse proxy (Caddy/nginx/Traefik) in front of `:8000` for HTTPS,\nand add a firewall + scheduled `pg_dump` backups. The MCP endpoint is `https:///mcp`.\n\n## Checking it with MCP Inspector\n\nThe standard visual way to exercise the tools:\n\n```powershell\n# stdio (local)\nnpx @modelcontextprotocol/inspector .\\.venv\\Scripts\\python.exe -m indo_usa_mcp.server\n\n# or point the Inspector at a running HTTP server: http://localhost:8000/mcp\n```\n\n## Guardrails honoured\n\n- Public data only (OSM/ODbL), no login-required scraping, no personal data, rate-limited.\n- Confidence scoring, soft deletes, full version history.\n- Human approval for high-risk updates; `is_featured` is an explicit, visible flag.\n\n## Status / roadmap\n\nPhase 1 is implemented and runs end-to-end: schema + versioning + approval queue, the\npipeline, two scrapers (OSM + Wikidata), all 5 MCP tools, semantic (pgvector) search,\noutreach & claiming, six autonomous agents with a scheduler, seed fixtures, and a hosted\ndeployment stack. Remaining \"last mile\": real outreach channel delivery, a claim web page,\nthe Approval-Assistant + Feedback agents, monetization logic, and production hardening\n(HTTPS, backups). Each future vertical (temples, events, groceries…) gets its own\ntable/scrapers/tools but shares this infra.\n","changelogMarkdown","videoKey","requiredSecretsJson","publishedAt","priceCents","trialDays","upstream",{"_98":99,"_100":-5,"_101":102,"_103":104,"_105":106,"_107":108},"requiredSecrets",[],"tags",[],"assets",[],"stats",{"_96":59,"_97":59},"ratingAvg","reviewCount","topics",[],"license","language","Python","registryName","us.namasteamerica/namaste-america","homepage","https://namasteamerica.us/for-agents","packages",[],"ENV",{"_111":112,"_113":114,"_115":116},"ENVIRONMENT","production","TURNSTILE_SITE_KEY","0x4AAAAAAB8-rEI2gNDf_QhU","SENTRY_DSN","https://45fd1229f78089d5c3ee78be128030e1@o4510583261757440.ingest.de.sentry.io/4510584253448272"]