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.
Available components
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
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
- TLS not yet verified: we couldn't open a connection to check the certificate. View diagnostics → Unverified
- Authorisation not yet verified: we couldn't confirm whether this endpoint requires it. View diagnostics → Unverified
- HTTPS is enforced; there's no plaintext access path. View diagnostics → Pass
- HSTS not yet verified: we couldn't read the response headers to check for it. View diagnostics → Unverified
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Transport & Reachability0
- Transport check failed: declared streamable-http, but we couldn't connect to verify it. See how to fix → View diagnostics → Fail
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.
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
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
Background: What to check on a remote MCP endpoint →
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 |
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 →
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.
| Name | Type | Req | Description |
|---|---|---|---|
| question | string | yes | Plain English question, e.g. "What restaurant categories are missing in 32082?" |
| zip | string | – | 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.'
| Name | Type | Req | Description |
|---|---|---|---|
| query_context | object | – | Optional: { agent_type, agent_id } |
| zip | string | yes | ZIP 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.
| Name | Type | Req | Description |
|---|---|---|---|
| note | string | – | Optional note to the business |
| response_id | string | yes | UUID of the response to accept |
| rfq_id | string | yes | UUID 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.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Max results (default 20) |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| focus | string | – | Ranking focus: "opportunity" (default), "hhi", "saturation", "growth", or "population". |
| limit | number | – | Max rows to return (default 10). |
| zips | array | yes | Array 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).
| Name | Type | Req | Description |
|---|---|---|---|
| booking_id | string | yes | UUID returned by local_intel_book |
| note | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| lat | number | – | Latitude (WGS84) — resolves to nearest FL ZIP |
| lon | number | – | Longitude (WGS84) |
| query | string | yes | Natural language question about construction market |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| lat | number | – | Latitude (WGS84) — resolves to nearest FL ZIP |
| lon | number | – | Longitude (WGS84) — required if lat is provided |
| radius_miles | number | – | Search radius in miles (default 1.0) |
| zip | string | – | 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".
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Max results (default 20) |
| street | string | yes | Street name (e.g. "A1A", "Palm Valley", "Crosswater") |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| reason | string | – | Optional reason for declining (e.g. price too high, too far) |
| response_id | string | yes | UUID of the response to decline |
| rfq_id | string | yes | UUID 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.
| Name | Type | Req | Description |
|---|---|---|---|
| agent_id | string | – | Your agent UUID for memory + delta computation |
| agent_type | string | – | real_estate | financial | ad_placement | logistics | business_owner | civic |
| budget | number | – | Agent budget in pathUSD (optional, for signal prioritization) |
| depth | string | – | quick (top 5 signals) | full (top 10 + context blocks) |
| intent | string | – | Plain-language description of what you are trying to decide or do |
| lat | number | – | Latitude (if no ZIP) |
| lon | number | – | Longitude (if no ZIP) |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| lat | number | – | Latitude (WGS84) — resolves to nearest FL ZIP |
| lon | number | – | Longitude (WGS84) |
| query | string | yes | Natural language question about healthcare market |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| category | string | – | Filter by OSM category |
| group | string | – | Filter by semantic group |
| lat | number | yes | Latitude of center point |
| limit | integer | – | Max results (default 15) |
| lon | number | yes | Longitude of center point |
| radius_miles | number | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| zip | string | yes | ZIP 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.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | number | – | Number of L1 ZIPs to return (default 5, max 10). |
| project_type | string | yes | Business type or project category. Examples: restaurant, clinic, banking, construction, real_estate, residential_development, fitness, legal, retail, grocery, auto, beauty, pets. |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| lat | number | – | Optional latitude (WGS84). Resolves to nearest FL ZIP. Use instead of zip for coordinate-based queries. |
| lon | number | – | Optional longitude (WGS84). Required if lat is provided. |
| query | string | yes | Any plain-English market question. ZIP can be in the query or passed separately. |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| lat | number | – | Latitude (WGS84) — resolves to nearest FL ZIP |
| lon | number | – | Longitude (WGS84) |
| query | string | yes | Natural language question (e.g. "What is the flood risk for this ZIP?", "What commercial gaps exist?") |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| lat | number | – | Latitude (WGS84) — resolves to nearest FL ZIP |
| lon | number | – | Longitude (WGS84) |
| query | string | yes | Natural language question about restaurant market |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| lat | number | – | Latitude (WGS84) — resolves to nearest FL ZIP |
| lon | number | – | Longitude (WGS84) |
| query | string | yes | Natural language question about retail market |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| autonomy | string | – | full=agent books automatically; approve=agent picks best, human confirms; human=human picks from list |
| budget_usd | number | – | Max budget in USD (optional) |
| business_id | string | – | Target a specific LocalIntel business (required when ordering from a named restaurant) |
| business_name | string | – | Human business name, e.g. McFlamingo — shown on the Jobs card |
| category | string | – | Business category to match, e.g. "restaurant", "food", "delivery", "landscaping", "florist", "handyman", "plumber" |
| customer_note | string | – | Extra note for the business (allergies, ETA, pickup vs delivery) |
| deadline_minutes | number | – | Minutes until deadline (for urgent delivery jobs) |
| description | string | yes | Full human-readable request. For food: include items, e.g. "Order for McFlamingo: chicken and broccoli". Do NOT use vague text like "Buy me." |
| dropoff_address | string | – | Drop-off address (delivery jobs) |
| dry_run | boolean | – | 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. |
| items | array | – | Structured line items, e.g. [{ "name": "chicken and broccoli", "qty": 1 }] |
| job_type | string | – | delivery = first-to-accept wins (food orders, pickups); proposal = collect quotes, pick best (services, construction) |
| notify_email | string | – | Email to notify for approve/human autonomy levels |
| pickup_address | string | – | Pickup address (delivery jobs) |
| task | string | – | Alias for description (same meaning) |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| rfq_id | string | yes | UUID 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).
| Name | Type | Req | Description |
|---|---|---|---|
| category | string | – | Exact OSM category (restaurant, bank, dentist...) |
| group | string | – | Semantic group: food | retail | health | finance | civic | services |
| limit | integer | – | Max results (default 20, max 50) |
| query | string | – | Text search on name/category/address |
| zip | string | – | 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.
| Name | Type | Req | Description |
|---|---|---|---|
| zip | string | yes | ZIP 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.
| Name | Type | Req | Description |
|---|---|---|---|
| agent_type | string | – | real_estate | financial | ad_placement | logistics | business_owner | civic |
| query_context | object | – | Optional: { agent_id, purpose } |
| zip | string | yes | ZIP 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.
| Name | Type | Req | Description |
|---|---|---|---|
| include_layers | array | – | Layers to include: bedrock, ocean_floor, surface_current, wave_surface (default: all) |
| query_context | object | – | Optional: { agent_type, agent_id, purpose } |
| zip | string | yes | ZIP 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.
| Name | Type | Req | Description |
|---|---|---|---|
| lat | number | – | Latitude (WGS84) — resolves to nearest FL ZIP |
| lon | number | – | Longitude (WGS84) — required if lat is provided |
| zip | string | – | FL ZIP code |
No output schema declared.
No examples provided.
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
claude mcp add --transport http mcflamingo-local-intel 'https://gsb-swarm-production.up.railway.app/api/local-intel/mcp'
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.