MCP Servers Integration

Pro AI The GEO29 API natively supports the Model Context Protocol (MCP), letting you plug high-fidelity geospatial data directly into AI assistants. Your agents can pull verified neighborhood data during conversations without any custom parsing or data pipelines.

Why MCP Over Grounding?

Most teams default to RAG/grounding pipelines when connecting external data to LLMs. For neighborhood intelligence, MCP is a better fit for two key reasons:

Trustworthy by Design

Grounding relies on embedding similarity to retrieve chunks, and a wrong chunk can surface a mismatched tax record, an outdated zoning law, or a neighboring parcel’s data. MCP eliminates that risk: your agent calls a typed endpoint with explicit coordinates, and the response is a single, authoritative record with source attribution. No retrieval ambiguity.

Lower Cost

Grounding pipelines inject large context windows of retrieved documents into every LLM call, and most providers charge a premium for grounding tokens. GEO29 MCP tool calls return compact, structured payloads (a score and a one-line description), adding minimal input tokens. No extra LLM API surcharges, no bloated context windows.

Connection Details

Our MCP servers use Streaming HTTP (SSE) for transport. You need two things to connect:

Your API token (issued on your account dashboard).
The server URLs for the intelligence domains you want to enable.

Available MCP Servers

We split tools into topical servers so you can grant your AI access only to the data it needs.

Server	URL	Capabilities
Schools	`https://api.ihuus.com/v1/mcp/schools/`	K-12 school search, ratings, district boundaries and district ratings
Vibe	`https://api.ihuus.com/v1/mcp/vibe/`	Privacy, walkability, visual appeal, dog friendliness, urban-rural character, liveliness
Environment	`https://api.ihuus.com/v1/mcp/environment/`	Noise levels, air quality, industrial proximity
Demographics	`https://api.ihuus.com/v1/mcp/demographics/`	Insurance coverage, ideological lean, population age profile
Risk	`https://api.ihuus.com/v1/mcp/risk/`	Flood risk (FEMA), fire risk (CALFIRE)
Tools	`https://api.ihuus.com/v1/mcp/tools/`	Address geocoding and other utility functions

The Tools Server

Most intelligence endpoints require lat/lon coordinates, but users typically provide street addresses or city names. The Tools server includes a geocoding endpoint that converts free-text addresses into coordinates, enabling your agent to work with our APIs out of the box. By connecting the Tools server alongside any intelligence server, your agent can handle the full workflow autonomously: receive an address from the user, geocode it, then query the relevant intelligence dimensions, all without the user needing to provide coordinates.

Pre-built Extensions

Don’t want to wire up MCP servers by hand? The ihuus/mcp GitHub repository ships ready-to-use extensions for Claude Desktop and Gemini CLI — no backend code required.

Claude Desktop

One-click .mcpb bundle. Download, open, paste your API key — all 18 tools available instantly.

Gemini CLI

Per-domain or all-in-one extensions. Clone the repo and link with a single gemini extensions link command.

Quick Install

Claude Desktop — no clone needed:

# 1. Download the bundle and open it in Claude Desktop
#    https://github.com/ihuus/mcp/raw/main/claude/desktop/mcpb.mcpb

# 2. Paste your API key when prompted — done.

Gemini CLI — clone and link:

git clone https://github.com/ihuus/mcp.git
cd mcp

export IHUUS_API_KEY=your_key_here    # https://ihuus.com/pricing
export GEMINI_API_KEY=your_key_here   # https://aistudio.google.com/apikey

gemini extensions link gemini/full
gemini

Example Output

The extensions include a neighborhood-analyst skill that produces structured, multi-domain reports. Here is a sample output for 250 Mariposa Ave, Mountain View, CA generated by Claude Desktop:

Sample neighborhood report for 250 Mariposa Ave, Mountain View, CA generated by Claude Desktop using iHuus tools

Full interactive report: static.ihuus.com/reports/neighborhood_report_250_mariposa_ave.html

Client Setup & Model Selection

These endpoints work with any modern MCP-compatible client: Open WebUI, Gemini Studio, GitHub Copilot, Claude Desktop, or your own custom agent.

Model selection tip: Our tools return highly structured, semantic descriptions, so fast “Lite” models (like Gemini 3.1 Flash Lite or Claude Sonnet 4.6) execute tool calls rapidly and pass data to users efficiently. Larger “Pro” or “Deep Thinking” models often overthink standard data lookups.

System Prompt

To get reliable results, constrain your agent’s behavior. LLMs will often hallucinate dummy coordinates if a user asks a vague question like “How are the schools in Texas?” The system prompt below forces the AI to rely strictly on GEO29 data and ask for precise locations when needed.

You are a helpful, knowledgeable, and conversational AI assistant specializing in
neighborhood analysis and educational landscapes.

## Capabilities and Boundaries

**General Knowledge**
- You may use internal knowledge to discuss general geographic topics, state policies,
  or historical context.
- If you use internal knowledge to discuss neighborhood characteristics, you MUST
  explicitly state that this is general knowledge and should be verified.

**Verified Data (Your Tools)**
- Your tools provide access to VERIFIED knowledge from trustworthy government and
  authoritative data sources. Tool output includes source attribution when available;
  highlight this to the user.
- Tools typically require coordinates. Use the geocoding tool to convert addresses,
  city names, or neighborhoods into latitude and longitude. Precise addresses produce
  higher-quality, block-level results.
- If tools are available to answer a query, you MUST use them first. You CAN augment
  tool output with general knowledge, but clearly distinguish verified tool data from
  your own knowledge.

## Rules for Tool Usage

1. **Rely on Metadata:** Read each tool's description, schema, and parameter annotations
   to understand exactly what inputs are needed. Do not assume.
2. **Never Guess or Hallucinate:** If the user's request is too broad or lacks required
   parameters, do not fabricate inputs to force a tool call.
3. **Ask Follow-up Questions:** If the tool requires specific inputs the user hasn't
   provided, politely ask a clarifying question before proceeding.
4. **Geocode First:** When a user provides an address or place name, convert it to
   coordinates using the geocoding tool before calling intelligence endpoints.

## Tone

Be empathetic, clear, and straightforward. Mirror the user's energy. Stay concise
unless detail is needed. Refer to tools as "my data" or "verified data sources."

## Data Sources and Citations

Tools may include a specific data source reference and vintage year. These are
HIGHLY AUTHORITATIVE. When providing facts from your own knowledge (not tools),
make it clear the information should be independently verified.

Testing Your Agent

Once your MCP servers are connected and your system prompt is applied, run through this conversation to verify the agent calls tools correctly: 1. Vague opener (agent should ask for a specific address, not hallucinate coordinates)

“Hey, I’m moving to Mountain View, CA. How is it there?”

2. Environment query (geocodes first via Tools, then triggers Environment server)

“I’ll be living near 250 Mariposa Ave. Is it noisy?”

3. Schools query (triggers Schools server — districts lookup, then school search)

“My kids are 10 and 15. How good are the schools around that address?”

4. Vibe query (triggers Vibe server)

“Will I be able to hear my neighbors, and is there a good place to walk my dog nearby?”

5. Risk query (triggers Risk server)

“Are there any flood or fire risks I should know about?”

6. Full neighborhood report (triggers all servers in parallel after geocoding — the best way to stress-test the full pipeline)

“Give me a complete neighborhood report for 250 Mariposa Ave, Mountain View, CA.”

A well-configured agent should geocode the address once, then call all domain tools in parallel and return a structured report covering Vibe & Character, Environment, Demographics, Risk, and Schools.

Getting Started

API Reference

Why MCP Over Grounding?

Trustworthy by Design

Lower Cost

Connection Details

Available MCP Servers

The Tools Server

Pre-built Extensions

Claude Desktop

Gemini CLI

Quick Install

Example Output

Client Setup & Model Selection

System Prompt

Testing Your Agent

​Why MCP Over Grounding?

Trustworthy by Design

Lower Cost

​Connection Details

​Available MCP Servers

​The Tools Server

​Pre-built Extensions

Claude Desktop

Gemini CLI

​Quick Install

​Example Output

​Client Setup & Model Selection

​System Prompt

​Testing Your Agent

Why MCP Over Grounding?

Connection Details

Available MCP Servers

The Tools Server

Pre-built Extensions

Quick Install

Example Output

Client Setup & Model Selection

System Prompt

Testing Your Agent