io.github.MCFLAMINGO/local-intel
REMOTE · GSB-SWARM-PRODUCTION.UP.RAILWAY.APP · SCANNED AUG 3
Hyperlocal business intelligence for AI agents. 20 MCP tools. Florida-first, Sunbelt expansion.
Available components
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 →
Endpoint Security57
- The endpoint's TLS certificate is valid, in date, and uses a strong key. View diagnostics → Pass
- Authorisation not fully verified: no authorisation is required to call this server, and 27 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe. See how to fix → View diagnostics → Unverified
- HTTPS is enforced; there's no plaintext access path. View diagnostics → Pass
- HSTS check failed: the Strict-Transport-Security header is absent. See how to fix → View diagnostics → Fail
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Transport & Reachability100
- Verified streamable-http transport via a live MCP handshake. View diagnostics → Pass
Schema Quality & AI Usability65
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 3672 tokens (~136/item across 27 items; 27 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management27
- Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Capabilities20
- Spec-recency check failed: implements MCP spec 2024-11-05; the latest is 2026-07-28. See how to fix → Fail
Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.
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
[mcp_servers.mcflamingo-local-intel] url = "https://gsb-swarm-production.up.railway.app/api/local-intel/mcp"
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"mcflamingo-local-intel": {
"type": "remote",
"url": "https://gsb-swarm-production.up.railway.app/api/local-intel/mcp",
"enabled": true
}
}
} openclaw mcp add mcflamingo-local-intel --url https://gsb-swarm-production.up.railway.app/api/local-intel/mcp --transport streamable-http
mcp_servers:
mcflamingo-local-intel:
url: "https://gsb-swarm-production.up.railway.app/api/local-intel/mcp" {
"mcpServers": {
"mcflamingo-local-intel": {
"type": "http",
"url": "https://gsb-swarm-production.up.railway.app/api/local-intel/mcp"
}
}
} The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.
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.
- 3 Aug 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.
- 1 Aug 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.
- 31 Jul 26 −2
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 30 Jul 26 +1
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 28 Jul 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.
- 27 Jul 26 +1
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 26 Jul 26 58
First indexed and scored.
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 3 Aug 2026 · Probed https://gsb-swarm-production.up.railway.app/api/local-intel/mcp
TLS valid
Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .
| Subject | Issuer | Valid from | Valid until | Key | Signature | Serial |
|---|---|---|---|---|---|---|
| CN=*.up.railway.app | CN=YE1,O=Let's Encrypt,C=US | 29 Jul 2026 | 27 Oct 2026 | ECDSA 256 | ECDSA-SHA384 | 6da79bb561da3efeb0e751ca21abd3999fe |
| SANs: *.up.railway.app, up.railway.app | ||||||
| CN=YE1,O=Let's Encrypt,C=US (CA) | CN=Root YE,O=ISRG,C=US | 3 Sept 2025 | 2 Sept 2028 | ECDSA 384 | ECDSA-SHA384 | 5ddd70dd31f801c85c186a7a04b80afe |
| CN=Root YE,O=ISRG,C=US (CA) | CN=ISRG Root X2,O=Internet Security Research Group,C=US | 13 May 2026 | 2 Sept 2032 | ECDSA 384 | ECDSA-SHA384 | 872165fc34b6e5fba8add5b3705fb53a |
| CN=ISRG Root X2,O=Internet Security Research Group,C=US (CA) | CN=ISRG Root X1,O=Internet Security Research Group,C=US | 13 May 2026 | 2 Sept 2032 | ECDSA 384 | SHA256-RSA | 6c8f1dc727c7117f7baf853ac980f9cd |
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 No authorisation required
The endpoint answered without asking for a token. Anyone who knows the URL can reach it.
| Result | No authorisation required |
|---|---|
| HTTP status | 200 |
Transports 3 probes
| Transport | URL | Outcome | Status | Location |
|---|---|---|---|---|
| streamable-http | https://gsb-swarm-production.up.railway.app/api/local-intel/mcp | Verified | 200 | |
| sse | https://gsb-swarm-production.up.railway.app/api/local-intel/mcp | Not MCP | 200 | |
| http (plaintext) | http://gsb-swarm-production.up.railway.app/api/local-intel/mcp | HTTPS enforced | 301 | https://gsb-swarm-production.up.railway.app/api/local-intel/mcp |
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.
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.