Hyperlocal business intelligence for AI agents. 20 MCP tools. Florida-first, Sunbelt expansion.
| Endpoint | https://gsb-swarm-production.up.railway.app/api/local-intel/mcp (streamable-http) |
|---|---|
| Registry version | 1.2.0 |
| Now | 27 tools · grade D (61) · pin ts_2dfdda53732e3d6bc39373e6c8144474 · protocol 2024-11-05 |
| First / last listed | 2026-10-28 02:06 UTC / 2026-10-28 02:06 UTC |
| Grade over time | 2026-10-28: D (61), 27 tools |
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Tools: local_intel_query local_intel_context local_intel_search local_intel_nearby local_intel_zone local_intel_corridor local_intel_changes local_intel_stats local_intel_tide local_intel_signal local_intel_bedrock local_intel_for_agent local_intel_oracle local_intel_sector_gap local_intel_realtor local_intel_healthcare local_intel_retail local_intel_construction local_intel_restaurant local_intel_ask local_intel_compare local_intel_project local_intel_rfq local_intel_rfq_status local_intel_book local_intel_decline_response local_intel_complete
local_intel_query | START HERE. Natural language entry point for both market intelligence AND business routing. Ask about a market, find a business, or route a customer request. Auto-detects ZIP, industry vertical, and intent. For customer agents: "Find a restaurant in 32082 that serves lunch" or "Who can do landscaping in Ponte Vedra?" — returns the matching business so your agent can route the order to them. For ma params: query*, zip, lat, lon |
local_intel_context | Full spatial context block for any FL zip or lat/lon. Returns anchor business, nearby businesses in distance rings, zone intelligence, and category breakdown. Best first call for any location query. Covers all 1,473 FL ZIPs via fl_zip_geo. params: zip, lat, lon, radius_miles |
local_intel_search | Search businesses by name, category, or semantic group (food, retail, health, finance, civic, services). params: zip, query, category, group, limit |
local_intel_nearby | Find businesses within a radius of any lat/lon point, sorted by distance with compass bearing. params: lat*, lon*, radius_miles, category, group, limit |
local_intel_zone | Spending zone and demographic data for a ZIP code: population, income, home value, rent, ownership rate, zone score. Pass zip or lat/lon. params: zip, lat, lon |
local_intel_corridor | Businesses along a named street corridor. Use for queries like "what is on A1A" or "businesses on Palm Valley Road". params: street*, zip, limit |
local_intel_changes | Recently added or owner-verified business listings. Use to detect new openings or data updates. params: zip, limit |
local_intel_stats | Dataset coverage stats: total businesses, confidence scores, query volume, revenue earned. params: none |
local_intel_tide | Tidal reading for a ZIP — temperature (0-100), direction (surging/heating/stable/cooling/receding), seasonal context. Synthesizes all 4 data layers. Best for agents deciding WHERE to act next. params: zip*, include_layers, query_context |
local_intel_signal | Investment and activity signal for a ZIP. Composite score 0-100 with band (strong_buy/accumulate/hold/reduce/avoid), top reasons, and avoid flags. Best for real estate and financial agents. params: zip*, agent_type, query_context |
local_intel_bedrock | Infrastructure momentum score and active leading indicators for a ZIP from Layer 0. Permits, road projects, flood zones, utility extensions. Predicts conditions 12-36 months ahead. 'Let Google pay for the satellites — we sell the weather forecast.' params: zip*, query_context |
local_intel_for_agent | PREMIUM composite entry point ($0.05). Declare your agent_type and intent, receive pre-ranked top-10 signals assembled from all 4 data layers, personalized for your use case. Includes delta since your last query if agent_id provided. Best first call for any new agent. params: agent_type, intent, zip, lat, lon, budget, depth, agent_id |
local_intel_oracle | Pre-baked economic oracle for a ZIP. Returns: restaurant saturation (is there room for another?), price-tier gap analysis (what menu price is missing?), growth trajectory (growing/empty-nest/stable), and 3 pre-formed questions with answers baked in. No LLM needed — answers derived from population, income, business density, school count, and infrastructure signals. params: zip* |
local_intel_sector_gap | Ranked sector gap analysis for a ZIP. Identifies NAICS sectors present at county level (CBP/CES employment) but underrepresented at ZIP (OSM business counts) — the structural whitespace in a local economy. Returns ranked opportunities with: NAICS code, sector label, county employment share, demand estimate, confidence tier, and LLM-ready signal narrative. Reads live from Postgres zip_signals — alw params: zip* |
local_intel_realtor | Real estate intelligence for a ZIP. Ask natural-language questions: demographics, commercial gaps, flood risk, school proximity, infrastructure signals, market saturation. Returns structured data with confidence score. Trained on 100 realtor use-case prompts. params: query*, zip, lat, lon |
local_intel_healthcare | Healthcare market intelligence for a ZIP. Ask about provider density, patient demographics, demand gaps, senior population. Returns structured data with confidence score. Trained on 100 healthcare business prompts. params: query*, zip, lat, lon |
local_intel_retail | Retail market intelligence for a ZIP. Ask about store categories, spending capture rates, consumer profile, undersupplied niches. Returns structured data with confidence score. Trained on 100 retail business prompts. params: query*, zip, lat, lon |
local_intel_construction | Construction and home services market intelligence for a ZIP. Ask about contractor density, active permits, housing starts, population growth driving demand. Returns structured data with confidence score. Trained on 100 construction business prompts. params: query*, zip, lat, lon |
local_intel_restaurant | Restaurant and food service market intelligence for a ZIP. Ask about saturation scores, price-tier gaps, capture rates, corridor analysis, tidal momentum. Returns structured data with confidence score. Trained on 100 restaurant business prompts. params: query*, zip, lat, lon |
local_intel_ask | Composite NL query layer. Ask any plain-English question about a ZIP — demographics, market opportunity, restaurant gaps, retail saturation, construction activity, investment signals, healthcare, corridor analysis, recent changes, nearby businesses. Routes internally to the right tools and returns a synthesized, sourced answer with confidence score. Best single entry point for humans and LLMs. params: question*, zip |
local_intel_compare | Compare up to 10 ZIP codes side-by-side and get a ranked opportunity table. Returns per-ZIP signals (HHI, capture rate, infra momentum, consumer profile, top gap) plus a top_pick recommendation with reasoning. Best tool for site selection, franchise expansion, investment screening, and market prioritization. params: zips*, focus, limit |
local_intel_project | Project-type intelligence: pass a project_type (restaurant, clinic, banking, construction, real_estate, residential_development, fitness, legal, retail, auto, etc.) and get L1 ZIPs ranked by market or residential opportunity score plus L2 matching verified businesses already operating in that sector. Returns sector gap counts, HHI, population, growth state, and new-build %. Best tool for site sele params: project_type*, zip, limit |
local_intel_rfq | Route a customer request to local businesses — food orders, delivery, services, or any job. ALWAYS include the full order or ask in description (or items[]), plus business_id/business_name when ordering from a specific place. Never send a vague description like "Buy me." Use this when KDS/POS is off or for quote collection. Supports delivery (first-to-accept) and proposal (collect quotes) modes. params: job_type, category, zip, description*, task, items, business_id, business_name, customer_note, pickup_address, dropoff_address, budget_usd, deadline_minutes, autonomy, notify_email, dry_run |
local_intel_rfq_status | Poll the status of an RFQ. Returns the original request, all responses received so far, and booking details if booked. params: rfq_id* |
local_intel_book | Book a specific response to an RFQ — confirms the job with that business. Use after reviewing local_intel_rfq_status responses. params: rfq_id*, response_id*, note |
local_intel_decline_response | Decline a specific response to an RFQ and get the next in queue. Use when a client rejects the first responder — returns the next pending response automatically. First come first served queue. params: rfq_id*, response_id*, reason |
local_intel_complete | Mark a booked job as complete and settle payment to the local merchant wallet (Tempo pathUSD when SETTLEMENT_ENABLED=true; otherwise records settled_intent and feeds the forecast loop). params: booking_id*, note |
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