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io.github.MCFLAMINGO/local-intel

REMOTE · GSB-SWARM-PRODUCTION.UP.RAILWAY.APP

Hyperlocal business intelligence for AI agents. 20 MCP tools. Florida-first, Sunbelt expansion.

0 this week 75 Trust /100

Last scanned (9 hours ago)

Scores higher than 64% of remote servers

Unreachable for 1 day in a row. Last reached 4 Nov 2026.

Category Maps and Location

Trust breakdown (7 categories)

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score → Why this is hard to score →

Endpoint Security11
Transport & Reachability0
Schema Quality & AI Usability0
  • Schema not yet verified: we couldn't read the endpoint's schema, or could read only part of its tool list.Unverified
Stability & Change Management0
  • Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified
Tool Coverage0
  • Tool coverage not yet verified: we couldn't read the endpoint's tools, or could read only part of the list.Unverified
Tool Safety0
  • Tool safety not yet verified: we couldn't read the endpoint's tools, or could read only part of the list.Unverified
Capabilities0
  • Capabilities not yet verified: we couldn't read the endpoint's capabilities.Unverified

Unverified: 5 categories

Categories scored 0 because we could not verify them: authentication we do not have, an unreachable endpoint, or not enough scan history. We only credit what we can confirm.

Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 5 Nov 26 0
    • Endpoint reachability: reachable → unreachable ▼ security
    • HSTS header: fail → unverified ▼ security
    • Stability: pass → unverified ▼ security
    • Tool safety: pass → unverified ▼ security
    • TLS certificate: pass → unverified ▼ security
    • Transport: pass → fail ▼ security
    • Authorization: Authorisation not yet verified: we couldn't confirm whether this endpoint requires it. security
    • Capabilities: fail → unverified ▼ functional
    • Tool coverage: 100 → unverified ▼ functional
    • First check of Schema quality: unverified functional
  • 4 Nov 26 0
    • This server's schema is too large to store in full, so we cannot compare its tools day to day functional
  • 3 Nov 26 0
    • This server's schema is too large to store in full, so we cannot compare its tools day to day functional
  • 2 Nov 26 0
    • This server's schema is too large to store in full, so we cannot compare its tools day to day functional
  • 1 Nov 26 0
    • This server's schema is too large to store in full, so we cannot compare its tools day to day functional
  • 31 Oct 26 0
    • This server's schema is too large to store in full, so we cannot compare its tools day to day functional
  • 30 Oct 26 0
    • This server's schema is too large to store in full, so we cannot compare its tools day to day functional
  • 29 Oct 26 0
    • This server's schema is too large to store in full, so we cannot compare its tools day to day functional
Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 5 Nov 2026 · Probed https://gsb-swarm-production.up.railway.app/api/local-intel/mcp

TLS unreached
DNSSEC insecure

Validation of gsb-swarm-production.up.railway.app. — Not signed

Zone DS Keys Algorithms Outcome
. trust_anchor 20326, 38696 8, 8 Verified
app. present 23684 8 Verified
railway.app. absent Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation
Authentication Inconclusive

We could not reach the endpoint well enough to judge its authorisation posture.

Result Inconclusive

Background: How OAuth 2.1 works in the 2026 MCP spec →

Transports 3 probes
Transport URL Outcome Status Location
streamable-http https://gsb-swarm-production.up.railway.app/api/local-intel/mcp Unreachable
sse https://gsb-swarm-production.up.railway.app/api/local-intel/mcp Unreachable
http (plaintext) http://gsb-swarm-production.up.railway.app/api/local-intel/mcp HTTPS enforced
MCP tools · 27 exposed · ~3,467 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →

Tool Tokens
local_intel_ask ~130

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.

NameTypeReqDescription
questionstringyesPlain English question, e.g. "What restaurant categories are missing in 32082?"
zipstring–ZIP code (optional — will be extracted from question if present, defaults to 32082)

No output schema declared.

No examples provided.

local_intel_bedrock ~88

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.'

NameTypeReqDescription
query_contextobject–Optional: { agent_type, agent_id }
zipstringyesZIP code

No output schema declared.

No examples provided.

local_intel_book ~77

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

NameTypeReqDescription
notestring–Optional note to the business
response_idstringyesUUID of the response to accept
rfq_idstringyesUUID of the RFQ

No output schema declared.

No examples provided.

local_intel_changes ~51

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

NameTypeReqDescription
limitinteger–Max results (default 20)
zipstring–Optional ZIP filter

No output schema declared.

No examples provided.

local_intel_compare ~150

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.

NameTypeReqDescription
focusstring–Ranking focus: "opportunity" (default), "hhi", "saturation", "growth", or "population".
limitnumber–Max rows to return (default 10).
zipsarrayyesArray of ZIP codes to compare, e.g. ["32082","32081","32084"]. Max 10.

No output schema declared.

No examples provided.

local_intel_complete ~72

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).

NameTypeReqDescription
booking_idstringyesUUID returned by local_intel_book
notestring–Completion note or rating

No output schema declared.

No examples provided.

local_intel_construction ~108

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.

NameTypeReqDescription
latnumber–Latitude (WGS84) — resolves to nearest FL ZIP
lonnumber–Longitude (WGS84)
querystringyesNatural language question about construction market
zipstring–ZIP code to analyze

No output schema declared.

No examples provided.

local_intel_context ~130

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.

NameTypeReqDescription
latnumber–Latitude (WGS84) — resolves to nearest FL ZIP
lonnumber–Longitude (WGS84) — required if lat is provided
radius_milesnumber–Search radius in miles (default 1.0)
zipstring–Any FL ZIP code

No output schema declared.

No examples provided.

local_intel_corridor ~87

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

NameTypeReqDescription
limitinteger–Max results (default 20)
streetstringyesStreet name (e.g. "A1A", "Palm Valley", "Crosswater")
zipstring–Optional ZIP filter

No output schema declared.

No examples provided.

local_intel_decline_response ~99

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.

NameTypeReqDescription
reasonstring–Optional reason for declining (e.g. price too high, too far)
response_idstringyesUUID of the response to decline
rfq_idstringyesUUID of the RFQ

No output schema declared.

No examples provided.

local_intel_for_agent ~202

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.

NameTypeReqDescription
agent_idstring–Your agent UUID for memory + delta computation
agent_typestring–real_estate | financial | ad_placement | logistics | business_owner | civic
budgetnumber–Agent budget in pathUSD (optional, for signal prioritization)
depthstring–quick (top 5 signals) | full (top 10 + context blocks)
intentstring–Plain-language description of what you are trying to decide or do
latnumber–Latitude (if no ZIP)
lonnumber–Longitude (if no ZIP)
zipstring–Target ZIP code

No output schema declared.

No examples provided.

local_intel_healthcare ~103

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.

NameTypeReqDescription
latnumber–Latitude (WGS84) — resolves to nearest FL ZIP
lonnumber–Longitude (WGS84)
querystringyesNatural language question about healthcare market
zipstring–ZIP code to analyze

No output schema declared.

No examples provided.

local_intel_nearby ~108

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

NameTypeReqDescription
categorystring–Filter by OSM category
groupstring–Filter by semantic group
latnumberyesLatitude of center point
limitinteger–Max results (default 15)
lonnumberyesLongitude of center point
radius_milesnumber–Search radius in miles (default 0.5)

No output schema declared.

No examples provided.

local_intel_oracle ~106

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.

NameTypeReqDescription
zipstringyesZIP code to analyze (e.g. 32081)

No output schema declared.

No examples provided.

local_intel_project ~202

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.

NameTypeReqDescription
limitnumber–Number of L1 ZIPs to return (default 5, max 10).
project_typestringyesBusiness type or project category. Examples: restaurant, clinic, banking, construction, real_estate, residential_development, fitness, legal, retail, grocery, auto, beauty, pets.
zipstring–Optional. Filter L2 businesses to a specific ZIP. If omitted, returns top ZIPs ranked by score.

No output schema declared.

No examples provided.

local_intel_query ~221

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.

NameTypeReqDescription
latnumber–Optional latitude (WGS84). Resolves to nearest FL ZIP. Use instead of zip for coordinate-based queries.
lonnumber–Optional longitude (WGS84). Required if lat is provided.
querystringyesAny plain-English market question. ZIP can be in the query or passed separately.
zipstring–Optional ZIP override. If omitted, ZIP is detected from the query or resolved from lat/lon.

No output schema declared.

No examples provided.

local_intel_realtor ~129

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.

NameTypeReqDescription
latnumber–Latitude (WGS84) — resolves to nearest FL ZIP
lonnumber–Longitude (WGS84)
querystringyesNatural language question (e.g. "What is the flood risk for this ZIP?", "What commercial gaps exist?")
zipstring–ZIP code to analyze

No output schema declared.

No examples provided.

local_intel_restaurant ~110

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.

NameTypeReqDescription
latnumber–Latitude (WGS84) — resolves to nearest FL ZIP
lonnumber–Longitude (WGS84)
querystringyesNatural language question about restaurant market
zipstring–ZIP code to analyze

No output schema declared.

No examples provided.

local_intel_retail ~106

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.

NameTypeReqDescription
latnumber–Latitude (WGS84) — resolves to nearest FL ZIP
lonnumber–Longitude (WGS84)
querystringyesNatural language question about retail market
zipstring–ZIP code to analyze

No output schema declared.

No examples provided.

local_intel_rfq ~487

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.

NameTypeReqDescription
autonomystring–full=agent books automatically; approve=agent picks best, human confirms; human=human picks from list
budget_usdnumber–Max budget in USD (optional)
business_idstring–Target a specific LocalIntel business (required when ordering from a named restaurant)
business_namestring–Human business name, e.g. McFlamingo — shown on the Jobs card
categorystring–Business category to match, e.g. "restaurant", "food", "delivery", "landscaping", "florist", "handyman", "plumber"
customer_notestring–Extra note for the business (allergies, ETA, pickup vs delivery)
deadline_minutesnumber–Minutes until deadline (for urgent delivery jobs)
descriptionstringyesFull human-readable request. For food: include items, e.g. "Order for McFlamingo: chicken and broccoli". Do NOT use vague text like "Buy me."
dropoff_addressstring–Drop-off address (delivery jobs)
dry_runboolean–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.
itemsarray–Structured line items, e.g. [{ "name": "chicken and broccoli", "qty": 1 }]
job_typestring–delivery = first-to-accept wins (food orders, pickups); proposal = collect quotes, pick best (services, construction)
notify_emailstring–Email to notify for approve/human autonomy levels
pickup_addressstring–Pickup address (delivery jobs)
taskstring–Alias for description (same meaning)
zipstring–ZIP code to search businesses in

No output schema declared.

No examples provided.

local_intel_rfq_status ~54

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

NameTypeReqDescription
rfq_idstringyesUUID returned by local_intel_rfq

No output schema declared.

No examples provided.

local_intel_search ~112

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

NameTypeReqDescription
categorystring–Exact OSM category (restaurant, bank, dentist...)
groupstring–Semantic group: food | retail | health | finance | civic | services
limitinteger–Max results (default 20, max 50)
querystring–Text search on name/category/address
zipstring–Filter by ZIP code

No output schema declared.

No examples provided.

local_intel_sector_gap ~199

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.

NameTypeReqDescription
zipstringyesZIP code to analyze (e.g. 32081, 32082, 32259)

No output schema declared.

No examples provided.

local_intel_signal ~102

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.

NameTypeReqDescription
agent_typestring–real_estate | financial | ad_placement | logistics | business_owner | civic
query_contextobject–Optional: { agent_id, purpose }
zipstringyesZIP code

No output schema declared.

No examples provided.

local_intel_stats ~26

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

Input schema present but exposes no named parameters.

No output schema declared.

No examples provided.

local_intel_tide ~118

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.

NameTypeReqDescription
include_layersarray–Layers to include: bedrock, ocean_floor, surface_current, wave_surface (default: all)
query_contextobject–Optional: { agent_type, agent_id, purpose }
zipstringyesZIP code to read tidal state for

No output schema declared.

No examples provided.

local_intel_zone ~90

Spending zone and demographic data for a ZIP code: population, income, home value, rent, ownership rate, zone score. Pass zip or lat/lon.

NameTypeReqDescription
latnumber–Latitude (WGS84) — resolves to nearest FL ZIP
lonnumber–Longitude (WGS84) — required if lat is provided
zipstring–FL ZIP code

No output schema declared.

No examples provided.

Install

How do I install the io.github.MCFLAMINGO/local-intel MCP server?

io.github.MCFLAMINGO/local-intel is a hosted endpoint at https://gsb-swarm-production.up.railway.app/api/local-intel/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

remote · gsb-swarm-production.up.railway.app

# add to Claude Code
claude mcp add --transport http mcflamingo-local-intel 'https://gsb-swarm-production.up.railway.app/api/local-intel/mcp'
Common questions

What is the io.github.MCFLAMINGO/local-intel MCP server?

io.github.MCFLAMINGO/local-intel is an MCP server listed in the public MCP registry as io.github.MCFLAMINGO/local-intel. Hyperlocal business intelligence for AI agents. 20 MCP tools. Florida-first, Sunbelt expansion. This page covers its hosted endpoint (https://gsb-swarm-production.up.railway.app/api/local-intel/mcp).

Is the io.github.MCFLAMINGO/local-intel MCP server safe to use?

io.github.MCFLAMINGO/local-intel scores 75 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

What tools does the io.github.MCFLAMINGO/local-intel MCP server expose?

io.github.MCFLAMINGO/local-intel exposes 27 tools: local_intel_query, local_intel_context, local_intel_search, local_intel_nearby, local_intel_zone, and 22 more. Their descriptions and schemas cost roughly 3,467 tokens of context every time the server is loaded.

Does the io.github.MCFLAMINGO/local-intel MCP server require authentication?

Its publisher declares no required credentials for io.github.MCFLAMINGO/local-intel. We have not been able to confirm that against the live endpoint, and a server can require authorisation without declaring it here.

Is the io.github.MCFLAMINGO/local-intel MCP server still maintained?

io.github.MCFLAMINGO/local-intel is still listed in the MCP registry, though our most recent checks did not reach this channel. We last reached this channel on 4 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.