Local Intel

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

• Conexión: Se ejecuta en la nube
• Transporte: Streamable HTTP, SSE
• Categoría: Otros

Instalación

Elige la app de IA que usas y copia la configuración.

Pasó 3/3 revisiones · Lo que preguntas pasa por el servidor del autor (gsb-swarm-production.up.railway.app)

claude mcp add --transport http local-intel https://gsb-swarm-production.up.railway.app/api/local-intel/mcp

{
  "mcpServers": {
    "local-intel": {
      "url": "https://gsb-swarm-production.up.railway.app/api/local-intel/mcp"
    }
  }
}

{
  "servers": {
    "local-intel": {
      "type": "http",
      "url": "https://gsb-swarm-production.up.railway.app/api/local-intel/mcp"
    }
  }
}

Qué apps pueden usarlo

Según cómo se conecta este servidor y la documentación de cada app, comprobado el 10 nov 2026.

Se conectan por internet en

ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast

Todas las apps, con ventajas y desventajas →

Qué puede hacer

Nombres y descripciones de las herramientas tal como los informó el servidor el 10 nov 2026. Los escribió el creador, no nosotros; los mostramos como texto sin formato.

• 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 "W

• 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.

• local_intel_search

  Search businesses by name, category, or semantic group (food, retail, health, finance, civic, services).

• local_intel_nearby

  Find businesses within a radius of any lat/lon point, sorted by distance with compass bearing.

• 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.

• local_intel_corridor

  Businesses along a named street corridor. Use for queries like "what is on A1A" or "businesses on Palm Valley Road".

• local_intel_changes

  Recently added or owner-verified business listings. Use to detect new openings or data updates.

• local_intel_stats

  Dataset coverage stats: total businesses, confidence scores, query volume, revenue earned.

• 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.

• 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.

• 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.'

• 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.

• 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 derive

• 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 employ

• 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.

• 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.

• 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.

• 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.

• 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.

• 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

• 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 screenin

• 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 oper

• 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

• local_intel_rfq_status

  Poll the status of an RFQ. Returns the original request, all responses received so far, and booking details if booked.

• local_intel_book

  Book a specific response to an RFQ — confirms the job with that business. Use after reviewing local_intel_rfq_status responses.

• 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.

• 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).

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 11 nov 2026

• ✓ Superada

  El creador coincide con el repo

  El nombre en el registro se verificó con la cuenta de GitHub MCFLAMINGO, dueña del repo

• … Aún sin revisar

  Sigue en mantenimiento

  Todavía no hemos leído este repo en GitHub

• … Aún sin revisar

  Tiene licencia

  Todavía no hemos leído este repo en GitHub

• ✓ Superada

  El endpoint responde

  Respondió en 302 ms (HTTP 200)

• – No aplica

  Inicio de sesión con OAuth

  No hace falta iniciar sesión

• ✓ Superada

  Sin instrucciones ocultas en las descripciones de herramientas

  Leímos 27 descripciones de herramientas y no encontramos instrucciones ocultas para la IA

Reseñas de usuarios

Aún no hay reseñas

Inicia sesión con Google para escribir una reseña

Comparado con otros en Otrosi

• Más estrellas que el 0% de los otros 19178 servidores de esta categoría
• Publicado en el registro más recientemente que el 6% de los otros 19178 servidores de esta categoría
• Revisiones básicas iguales o mejores que el 100% de los otros 19178 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

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Cómo leer una tarjeta:↓ Descargas por semanai★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

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README del creador

No se encontró un README en el repo