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local-intel

io.github.MCFLAMINGO/local-intel
Business & Operations Maps & Location Real Estate Public & reachable MCP 2026-07-28

What this MCP does

Provides Florida-focused local business discovery, ZIP-code market intelligence, site-selection analysis, infrastructure signals, and routing of customer requests to businesses.

Capabilities

Tools

27 tools
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.
Input schema 
{'type': 'object', 'required': ['question'], 'properties': {'zip': {'type': 'string', 'description': 'ZIP code (optional — will be extracted from question if present, defaults to 32082)'}, 'question': {'type': 'string', 'description': 'Plain English question, e.g. "What restaurant categories are missing in 32082?"'}}}
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.'
Input schema 
{'type': 'object', 'required': ['zip'], 'properties': {'zip': {'type': 'string', 'description': 'ZIP code'}, 'query_context': {'type': 'object', 'description': 'Optional: { agent_type, agent_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.
Input schema 
{'type': 'object', 'required': ['rfq_id', 'response_id'], 'properties': {'note': {'type': 'string', 'description': 'Optional note to the business'}, 'rfq_id': {'type': 'string', 'description': 'UUID of the RFQ'}, 'response_id': {'type': 'string', 'description': 'UUID of the response to accept'}}}
local_intel_changes
Recently added or owner-verified business listings. Use to detect new openings or data updates.
Input schema 
{'type': 'object', 'properties': {'zip': {'type': 'string', 'description': 'Optional ZIP filter'}, 'limit': {'type': 'integer', 'description': 'Max results (default 20)'}}}
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.
Input schema 
{'type': 'object', 'required': ['zips'], 'properties': {'zips': {'type': 'array', 'items': {'type': 'string'}, 'description': 'Array of ZIP codes to compare, e.g. ["32082","32081","32084"]. Max 10.'}, 'focus': {'type': 'string', 'description': 'Ranking focus: "opportunity" (default), "hhi", "saturation", "growth", or "population".'}, 'limit': {'type': 'number', 'description': 'Max rows to return (default 10).'}}}
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).
Input schema 
{'type': 'object', 'required': ['booking_id'], 'properties': {'note': {'type': 'string', 'description': 'Completion note or rating'}, 'booking_id': {'type': 'string', 'description': 'UUID returned by local_intel_book'}}}
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.
Input schema 
{'type': 'object', 'required': ['query'], 'properties': {'lat': {'type': 'number', 'description': 'Latitude (WGS84) — resolves to nearest FL ZIP'}, 'lon': {'type': 'number', 'description': 'Longitude (WGS84)'}, 'zip': {'type': 'string', 'description': 'ZIP code to analyze'}, 'query': {'type': 'string', 'description': 'Natural language question about construction market'}}}
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.
Input schema 
{'type': 'object', 'properties': {'lat': {'type': 'number', 'description': 'Latitude (WGS84) — resolves to nearest FL ZIP'}, 'lon': {'type': 'number', 'description': 'Longitude (WGS84) — required if lat is provided'}, 'zip': {'type': 'string', 'description': 'Any FL ZIP code'}, 'radius_miles': {'type': 'number', 'description': 'Search radius in miles (default 1.0)'}}}
local_intel_corridor
Businesses along a named street corridor. Use for queries like "what is on A1A" or "businesses on Palm Valley Road".
Input schema 
{'type': 'object', 'required': ['street'], 'properties': {'zip': {'type': 'string', 'description': 'Optional ZIP filter'}, 'limit': {'type': 'integer', 'description': 'Max results (default 20)'}, 'street': {'type': 'string', 'description': 'Street name (e.g. "A1A", "Palm Valley", "Crosswater")'}}}
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.
Input schema 
{'type': 'object', 'required': ['rfq_id', 'response_id'], 'properties': {'reason': {'type': 'string', 'description': 'Optional reason for declining (e.g. price too high, too far)'}, 'rfq_id': {'type': 'string', 'description': 'UUID of the RFQ'}, 'response_id': {'type': 'string', 'description': 'UUID of the response to decline'}}}
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.
Input schema 
{'type': 'object', 'properties': {'lat': {'type': 'number', 'description': 'Latitude (if no ZIP)'}, 'lon': {'type': 'number', 'description': 'Longitude (if no ZIP)'}, 'zip': {'type': 'string', 'description': 'Target ZIP code'}, 'depth': {'type': 'string', 'description': 'quick (top 5 signals) | full (top 10 + context blocks)'}, 'budget': {'type': 'number', 'description': 'Agent budget in pathUSD (optional, for signal prioritization)'}, 'intent': {'type': 'string', 'description': 'Plain-language description of what you are trying to decide or do'}, 'agent_id': {'type': 'string', 'description': 'Your agent UUID for memory + delta computation'}, 'agent_type': {'type': 'string', 'description': 'real_estate | financial | ad_placement | logistics | business_owner | civic'}}}
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.
Input schema 
{'type': 'object', 'required': ['query'], 'properties': {'lat': {'type': 'number', 'description': 'Latitude (WGS84) — resolves to nearest FL ZIP'}, 'lon': {'type': 'number', 'description': 'Longitude (WGS84)'}, 'zip': {'type': 'string', 'description': 'ZIP code to analyze'}, 'query': {'type': 'string', 'description': 'Natural language question about healthcare market'}}}
local_intel_nearby
Find businesses within a radius of any lat/lon point, sorted by distance with compass bearing.
Input schema 
{'type': 'object', 'required': ['lat', 'lon'], 'properties': {'lat': {'type': 'number', 'description': 'Latitude of center point'}, 'lon': {'type': 'number', 'description': 'Longitude of center point'}, 'group': {'type': 'string', 'description': 'Filter by semantic group'}, 'limit': {'type': 'integer', 'description': 'Max results (default 15)'}, 'category': {'type': 'string', 'description': 'Filter by OSM category'}, 'radius_miles': {'type': 'number', 'description': 'Search radius in miles (default 0.5)'}}}
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.
Input schema 
{'type': 'object', 'required': ['zip'], 'properties': {'zip': {'type': 'string', 'description': 'ZIP code to analyze (e.g. 32081)'}}}
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 selection and franchise expansion when you know the business type but not the ZIP.
Input schema 
{'type': 'object', 'required': ['project_type'], 'properties': {'zip': {'type': 'string', 'description': 'Optional. Filter L2 businesses to a specific ZIP. If omitted, returns top ZIPs ranked by score.'}, 'limit': {'type': 'number', 'description': 'Number of L1 ZIPs to return (default 5, max 10).'}, 'project_type': {'type': 'string', 'description': 'Business type or project category. Examples: restaurant, clinic, banking, construction, real_estate, residential_development, fitness, legal, retail, grocery, auto, beauty, pets.'}}}
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 market intel: "Is 32082 oversaturated with dentists?" ZIP is always required for routing — pass it explicitly or include it in the query.
Input schema 
{'type': 'object', 'required': ['query'], 'properties': {'lat': {'type': 'number', 'description': 'Optional latitude (WGS84). Resolves to nearest FL ZIP. Use instead of zip for coordinate-based queries.'}, 'lon': {'type': 'number', 'description': 'Optional longitude (WGS84). Required if lat is provided.'}, 'zip': {'type': 'string', 'description': 'Optional ZIP override. If omitted, ZIP is detected from the query or resolved from lat/lon.'}, 'query': {'type': 'string', 'description': 'Any plain-English market question. ZIP can be in the query or passed separately.'}}}
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.
Input schema 
{'type': 'object', 'required': ['query'], 'properties': {'lat': {'type': 'number', 'description': 'Latitude (WGS84) — resolves to nearest FL ZIP'}, 'lon': {'type': 'number', 'description': 'Longitude (WGS84)'}, 'zip': {'type': 'string', 'description': 'ZIP code to analyze'}, 'query': {'type': 'string', 'description': 'Natural language question (e.g. "What is the flood risk for this ZIP?", "What commercial gaps exist?")'}}}
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.
Input schema 
{'type': 'object', 'required': ['query'], 'properties': {'lat': {'type': 'number', 'description': 'Latitude (WGS84) — resolves to nearest FL ZIP'}, 'lon': {'type': 'number', 'description': 'Longitude (WGS84)'}, 'zip': {'type': 'string', 'description': 'ZIP code to analyze'}, 'query': {'type': 'string', 'description': 'Natural language question about restaurant market'}}}
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.
Input schema 
{'type': 'object', 'required': ['query'], 'properties': {'lat': {'type': 'number', 'description': 'Latitude (WGS84) — resolves to nearest FL ZIP'}, 'lon': {'type': 'number', 'description': 'Longitude (WGS84)'}, 'zip': {'type': 'string', 'description': 'ZIP code to analyze'}, 'query': {'type': 'string', 'description': 'Natural language question about retail market'}}}
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.
Input schema 
{'type': 'object', 'required': ['description'], 'properties': {'zip': {'type': 'string', 'description': 'ZIP code to search businesses in'}, 'task': {'type': 'string', 'description': 'Alias for description (same meaning)'}, 'items': {'type': 'array', 'items': {'type': 'object'}, 'description': 'Structured line items, e.g. [{ "name": "chicken and broccoli", "qty": 1 }]'}, 'dry_run': {'type': 'boolean', 'description': 'If true, match businesses but do NOT send email/SMS/push/rail notifications. Also auto-enabled for x-agent-id values starting with cursor-test-, test-, agent-test-, or dry-run.'}, 'autonomy': {'enum': ['full', 'approve', 'human'], 'type': 'string', 'description': 'full=agent books automatically; approve=agent picks best, human confirms; human=human picks from list'}, 'category': {'type': 'string', 'description': 'Business category to match, e.g. "restaurant", "food", "delivery", "landscaping", "florist", "handyman", "plumber"'}, 'job_type': {'enum': ['delivery', 'proposal'], 'type': 'string', 'description': 'delivery = first-to-accept wins (food orders, pickups); proposal = collect quotes, pick best (services, construction)'}, 'budget_usd': {'type': 'number', 'description': 'Max budget in USD (optional)'}, 'business_id': {'type': 'string', 'description': 'Target a specific LocalIntel business (required when ordering from a named restaurant)'}, 'description': {'type': 'string', 'description': 'Full human-readable request. For food: include items, e.g. "Order for McFlamingo: chicken and broccoli". Do NOT use vague text like "Buy me."'}, 'notify_email': {'type': 'string', 'description': 'Email to notify for approve/human autonomy levels'}, 'business_name': {'type': 'string', 'description': 'Human business name, e.g. McFlamingo — shown on the Jobs card'}, 'customer_note': {'type': 'string', 'description': 'Extra note for the business (allergies, ETA, pickup vs delivery)'}, 'pickup_address': {'type': 'string', 'description': 'Pickup address (delivery jobs)'}, 'dropoff_address': {'type': 'string', 'description': 'Drop-off address (delivery jobs)'}, 'deadline_minutes': {'type': 'number', 'description': 'Minutes until deadline (for urgent delivery jobs)'}}}
local_intel_rfq_status
Poll the status of an RFQ. Returns the original request, all responses received so far, and booking details if booked.
Input schema 
{'type': 'object', 'required': ['rfq_id'], 'properties': {'rfq_id': {'type': 'string', 'description': 'UUID returned by local_intel_rfq'}}}
local_intel_search
Search businesses by name, category, or semantic group (food, retail, health, finance, civic, services).
Input schema 
{'type': 'object', 'properties': {'zip': {'type': 'string', 'description': 'Filter by ZIP code'}, 'group': {'type': 'string', 'description': 'Semantic group: food | retail | health | finance | civic | services'}, 'limit': {'type': 'integer', 'description': 'Max results (default 20, max 50)'}, 'query': {'type': 'string', 'description': 'Text search on name/category/address'}, 'category': {'type': 'string', 'description': 'Exact OSM category (restaurant, bank, dentist...)'}}}
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 — always current. Example: "NAICS 62 Health Care: Jacksonville MSA 136k healthcare employees, ZIP 32082 has no OSM healthcare listings. 28,697 residents, $121k median HHI, retiree index 1.5x. Demand: 7–10 providers." Chain into vertical agents via oracle_vertical. Cost: $0.03 pathUSD.
Input schema 
{'type': 'object', 'required': ['zip'], 'properties': {'zip': {'type': 'string', 'description': 'ZIP code to analyze (e.g. 32081, 32082, 32259)'}}}
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.
Input schema 
{'type': 'object', 'required': ['zip'], 'properties': {'zip': {'type': 'string', 'description': 'ZIP code'}, 'agent_type': {'type': 'string', 'description': 'real_estate | financial | ad_placement | logistics | business_owner | civic'}, 'query_context': {'type': 'object', 'description': 'Optional: { agent_id, purpose }'}}}
local_intel_stats
Dataset coverage stats: total businesses, confidence scores, query volume, revenue earned.
Input schema 
{'type': 'object', 'properties': {}}
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.
Input schema 
{'type': 'object', 'required': ['zip'], 'properties': {'zip': {'type': 'string', 'description': 'ZIP code to read tidal state for'}, 'query_context': {'type': 'object', 'description': 'Optional: { agent_type, agent_id, purpose }'}, 'include_layers': {'type': 'array', 'description': 'Layers to include: bedrock, ocean_floor, surface_current, wave_surface (default: all)'}}}
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.
Input schema 
{'type': 'object', 'properties': {'lat': {'type': 'number', 'description': 'Latitude (WGS84) — resolves to nearest FL ZIP'}, 'lon': {'type': 'number', 'description': 'Longitude (WGS84) — required if lat is provided'}, 'zip': {'type': 'string', 'description': 'FL ZIP code'}}}
History

Recent tool changes

Added
local_intel_complete
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_decline_response
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_book
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_rfq_status
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_rfq
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_project
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_compare
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_ask
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_restaurant
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_construction
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_retail
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_healthcare
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_realtor
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_sector_gap
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_oracle
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_for_agent
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_bedrock
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_signal
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_tide
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_stats
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_changes
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_corridor
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_zone
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_nearby
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_search
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_context
Sept. 17, 2026, 12:43 p.m.
Added
local_intel_query
Sept. 17, 2026, 12:43 p.m.
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