Edwin Genego

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Real estate intelligence background

Domain MCP Reference

Real Estate MCP Server

Production-aligned protocol surface: 30+ tools, governed resources & deterministic prompt templates enabling acquisition, valuation & client advisory workflows without prompt sprawl.

  • Schema-first tool contracts
  • Deterministic prompt layer
  • Composable reasoning chains
  • Governed resource namespaces

Tools

30+

Resources

10+

Prompts

5+

Quick Navigation

Built for inspection, incremental extension & multi-model interoperability.

Real-World Workflow Examples

Complete end-to-end workflows demonstrating how real estate professionals leverage the MCP server through AI assistants for property analysis, client matching, and market intelligence.

Scenario

A real estate agent has a client looking for a Victorian home in Riverside under $800,000. The client needs at least 3 bedrooms, 2 bathrooms, and prefers areas with excellent schools for their growing family. The agent needs to quickly find matching properties, analyze market positioning, assess school districts, and provide a comprehensive report with property comparisons and investment potential - all while ensuring the search considers both current listings and recent comparable sales to provide accurate market context.

Scenario

An agent is preparing for a consultation with Sarah Johnson, a first-time homebuyer with a $600,000 budget. Sarah works downtown and wants a modern 2-3 bedroom property within easy commuting distance. As a first-time buyer, she needs education on the process, financing options, and market dynamics. The agent needs to match Sarah with suitable properties, prepare a comprehensive consultation framework covering buyer education, property options, and financial planning, while building a presentation that demonstrates market expertise and establishes trust for the long-term client relationship.

Scenario

An investor client is evaluating a potential multi-family property acquisition in the growing Riverside market. They need comprehensive market analysis covering current trends, rental yield potential, appreciation forecasts, and competitive positioning. The analysis must account for both immediate cash flow and long-term value appreciation, consider comparable recent sales, evaluate neighborhood development plans, and assess risk factors including market saturation and economic headwinds. The agent needs to deliver institutional-quality market intelligence that supports a data-driven investment decision within 48 hours.

Case Study / Exploration

This Real Estate MCP is a reference / exploratory implementation – a functioning case study validating protocol-driven AI capability surfaces (tools + resources + deterministic prompts). It is not a commercial product; the emphasis is architectural pattern proof, contract stability and cross-domain portability (now extended to relocation).

Interested in adapting the schema / layering for another vertical (finance, compliance, logistics) or extending multi-domain chaining? Reach out with scope & constraints and I can outline an incremental sequencing plan.

Iteration Update

This reference server seeded a second domain implementation

Core architecture, prompt scaffolds, tool taxonomy & dataset structuring patterns from this Real Estate MCP are now being reapplied to a Thailand Relocation MCP (visa pathways, budgeting, locality intelligence, settlement workflows). Reuse proves the portability of the protocol surface + schema-first layering.

We will publish a comparative evolution log (what changed, what stayed stable) after stabilizing the relocation dataset ingestion cycle.

Why a Protocol Surface (vs Ad-hoc RAG / Prompt Stuffing)

MCP formalizes AI interaction boundaries: instead of repeating brittle prompt bundles, structured tools + stable resources + deterministic prompts give you reproducible, governed reasoning. The real estate domain amplifies these benefits because multi-factor decisions (area fit, cost structure, ownership pathway, risk posture) demand selective, typed context - not token floods.

🧬Structured Fusion

Tools + prompts unify multi-factor property reasoning without token bloat.

🛰️Observable

Every call inspectable: parameters, latency, output schema.

♻️Low Drift

Deterministic templates + typed returns stabilize iteration cycles.

🧱Composable

Chainable primitives instead of monolithic opaque prompts.

Architecture Overview

Layered, schema-first design enabling deterministic tool invocation, composable resource retrieval and reasoning scaffolds through prompt templates.

🧱Protocol Layer

FastMCP server exposing tools, prompts & resources over SSE (future: WebSocket).

  • Tool registry
  • Prompt catalog
  • Resource resolver
  • Session state

🧩Domain Modules

Functional segmentation keeps reasoning coherent & reduces coupling across expansion.

  • Property module
  • Market module
  • Agent module
  • Client module

📂Resource Layer

Stable real-estate:// style namespaces for static + template resources.

  • Static JSON
  • Templated bundles
  • Version tags
  • Selective preload

MCP Inspector Views

Representative protocol-level inspection surfaces: resource registry, tool contract catalog, and prompt template layer.

MCP Inspector resources dashboard view
Resources / context registry
MCP Inspector tools list with semantic contracts
Tool contracts & invocation
MCP Inspector prompt templates view
Prompt template layer

Capability Domains

Functional groupings of tools and resources aligned to real-world decision & execution flows.

🏠

Property Intelligence

Search, comparables, structural attributes & value context.

  • Multi-criteria search
  • Comparable analysis
  • Listing normalization
  • Inventory snapshots
Search Valuation Inventory
📊

Market Analytics

Macro + micro market trends for investment or planning decisions.

  • Area metrics
  • Momentum signals
  • Pricing deltas
  • Historical trend merges
Trends Signals Ratios
🤝

Client & Agent Ops

Operational backbone for pairing, pipeline planning & performance context.

  • Preference mapping
  • Match scoring
  • Agent performance frames
  • Engagement metrics
Matching Performance Ops
🧾

Transaction Modeling

Closing cost estimation, ownership frameworks & structural pathways.

  • Ownership structures
  • Cost decomposition
  • Scenario bands
  • Risk factors
Costs Structures Scenarios
🛡️

Risk & Due Diligence

Checklist synthesis & advisory pattern outputs for safe acquisition.

  • Risk taxonomy
  • Checklist staging
  • Mitigation frames
  • Priority scoring
Checklist Advisory Mitigation
🗺️

Area Intelligence

Granular neighborhood / area descriptors, cost & lifestyle factors.

  • Amenity vectors
  • Lifestyle tags
  • Cost multipliers
  • Education overlays
Areas Lifestyle Cost

Schema-First Tool Contract Example

Every tool advertises an AI-legible contract (name, description, JSON schema). Below is an illustrative (condensed) contract powering property shortlist reasoning & subsequent cost / risk chaining.

{ "name": "search_properties", "description": "Search inventory with multi-criteria filters; returns scored shortlist for chaining cost + risk + area analysis.", "input_schema": { "type": "object", "properties": { "query": {"type": "string", "description": "Free-text phrase (optional)."}, "min_bedrooms": {"type": "integer", "minimum": 0}, "max_price": {"type": "integer", "minimum": 0}, "area": {"type": "string", "description": "Area slug (matches resources)."}, "limit": {"type": "integer", "default": 12, "maximum": 50} }, "additionalProperties": false }, "return": { "type": "object", "properties": { "results": { "type": "array", "items": { "type": "object", "properties": { "id": {"type": "string"}, "score": {"type": "number"}, "price": {"type": "integer"}, "area": {"type": "string"}, "bedrooms": {"type": "integer"}, "attributes": {"type": "object"} }, "required": ["id", "score", "price", "area"] } }, "meta": { "type": "object", "properties": { "query_time_ms": {"type": "integer"}, "total_matched": {"type": "integer"} } } }, "required": ["results"] } }

Representative Tool Catalog

Not exhaustive - demonstrates semantic range and JSON contract patterns.

🔍Search & Filtering

  • search_properties
  • filter_by_attributes
  • list_new_listings
  • compare_properties

Returns structured lists with scoring metadata.

📈Market Insight

  • get_market_overview
  • area_trend_snapshot
  • price_momentum
  • inventory_pressure

Aggregated & normalized numeric indicators.

🧾Transaction Costs

  • estimate_closing_costs
  • ownership_cost_bands
  • transaction_scenario_breakdown

Cost components tagged & typed for modeling.

🛡️Risk & Checklists

  • get_due_diligence_checklist
  • risk_factor_matrix
  • compliance_flags

Structured risk surfaces for prompt fusion.

🤝Client & Agent

  • match_client_preferences
  • agent_performance_summary
  • portfolio_gap_analysis

Operates on abstracted behavioral vectors.

🗺️Area Intelligence

  • get_area_details
  • area_cost_multipliers
  • amenity_cluster_summary

Supports lifestyle & return blending.

Transport Strategy: SSE First, STDIO Compatible

SSE chosen for multi-client accessibility & browser tooling. The implementation keeps transport abstraction thin so STDIO embedding (desktop AI) or future WebSocket upgrade remains low-friction.

🌐Multi-Client

SSE supports browsers, terminals & inspectors concurrently.

Low Overhead

No websocket handshake complexity; simple event stream.

🔌Desktop Ready

Abstraction allows STDIO swap for local embedded clients.

🛠️Inspectability

Curl & devtools friendly during iteration.

Incremental Roadmap

Focused expansion - prioritizing compound leverage over surface sprawl.

🧪Scenario Engine

Planned

Composable scenario deltas (rent vs buy vs hold).

  • Delta modeling
  • Assumption sets
  • Outcome scoring

🧬Feature Embeddings

Exploring

Vector attributes to enhance similarity & clustering.

  • Amenity embeddings
  • Lifestyle vectors
  • Cross-area mapping

📡Live Feed Hooks

Future

Optional ingestion of event-based market signals.

  • Price ticks
  • New listing deltas
  • Supply pressure

🛡️Policy Guardrails

Planned

Sensitive operation gating & escalation policies.

  • High-value lock
  • Audit tags
  • Redaction filters

🤖Auto-Test Harness

In Progress

Prompt + tool contract regression automation.

  • Snapshot baselines
  • Semantic drift alerts
  • Schema diff triggers

🗄️Delta Storage

Exploring

Temporal layering of resource changes.

  • Resource diff index
  • Time-travel queries
  • Staleness scoring

Implementation Layers

Three composable layers keep concerns separated and evolution safe: protocol surface (MCP contract), business logic (domain services, validation), and data integration (file, API, DB sources). Below: condensed examples mirroring production patterns.

🔌Protocol Layer

Registers tools, prompts & resources; enforces JSON schema; mediates transport (SSE / STDIO).

@server.list_tools()
async def list_tools():
    return [Tool(name="search_properties", description="Find properties", inputSchema={"type":"object"})]

⚙️Business Logic

Pure functions & services: filtering, scoring, market context enrichment, safety rules.

async def shortlist(props, max_price):
    return [p for p in props if p["price"] <= max_price]

🗂️Data Integration

Adapters unify CSV, API, scraped and cached datasets into normalized access patterns.

class PropertyRepo:
    async def search(self, **kw):
        # compose file + API layers
        ...

Tool Semantics & Design Principles

Tools model business intents, not transport or storage. Naming & description quality materially affects model invocation accuracy. Each contract answers: when should the model call me? what do I return? how risky is my side‑effect?

🎯Intent Naming

Prefer match_client_preferences over query_clients.

🧪Schema Guardrails

JSON schema reduces malformed calls & clarifies optional vs required parameters.

📦Structured Returns

Return small typed objects; avoid giant free‑text blocks for downstream chaining.

🔁Composable Granularity

Decompose: fetch → enrich → score; model can branch / skip steps.

Prompt Templates vs System Prompts

Prompt templates here are explicit, user-invoked analytical frames (investment analysis, comparative area study). They differ from opaque system prompts by being inspectable, versionable and parameterized.

Template Properties

  • Deterministic structure
  • Parameter binding (risk, horizon)
  • Low diff noise in review
  • Model-agnostic reuse

Why It Matters

  • Reduces hidden behavior drift
  • Encourages analytical consistency
  • Enables audit & governance
  • Supports multi-tool chaining clarity

Key Feature Groups

Surface breadth anchored to real workflows - each grouping aggregates narrowly scoped, composable tool contracts.

Property

7 tools

Agent

6 tools

Market

7 tools

Area Intel

9 tools

Client

3 tools

Security & Access Control

Guardrails treat every tool call as a permissioned action with observability. Narrow contracts + explicit auth + audit trail & rate limiting.

Authentication

Per-session tokens; rotation & revocation supported.

Authorization

Role → capability mapping; deny by default.

Audit & Telemetry

Structured logs: tool, latency, arguments hash.

Rate / Quotas

Adaptive ceilings by role & risk category.

Business Impact

Shift from ephemeral prompt stuffing to persistent capability surface: repeatable, inspectable, governed reasoning over your proprietary context.

Before MCP

  • Generic model responses
  • Context lost per turn
  • Manual data collation
  • Unbounded prompt drift

After MCP

  • +Live domain data access
  • +Composable reasoning chains
  • +Governed evolution & audit trail
  • +Faster feature iteration loops

Future & Getting Started

Emerging Directions

  • Multi-server orchestration
  • Autonomous action gating
  • Adaptive tool surfacing
  • Industry reference catalogs

Start Sequence

  1. Pick one narrow workflow
  2. Model 3–5 intent tools
  3. Add a single prompt template
  4. Integrate live data path
  5. Add auth + basic audit
  6. Ship & inspect; iterate

Context & Approach

This server extends a sequence of practical AI infrastructure explorations - moving from isolated prompt craft → targeted fine-tunes → protocol-governed integration. Each stage reinforced the same constraint: leverage arises from reliable, structured access to differentiated context.

Prompt Template Layer

Deterministic scaffolds orchestrating multi-tool reasoning frames - pure functions with predictable diff behavior.

🧪investment_opportunity_brief

Synthesizes property + market + risk vectors into a decision frame.

  • Structured sections
  • Risk emphasis
  • Scenario variants

🧪client_fit_report

Preference-weighted matching rationale & shortlist reasoning.

  • Score breakdown
  • Deltas
  • Next actions

🧪area_comparison_matrix

Factor-weighted cross-area differentials.

  • Factor weighting
  • Relative scoring
  • Suitability tags

🧪ownership_structure_advisor

Compares ownership paths by complexity, protection & cost.

  • Structure matrix
  • Decision criteria
  • Risk flags

🧪due_diligence_brief

Phase-indexed checklist with escalation notes.

  • Phase grouping
  • Flag severity
  • Mitigation guidance

Data & Resource Model

Stable resource namespaces & incremental refresh patterns keep intelligence fresh without ballooning prompt budgets.

📂Static Datasets

Canonical JSON snapshots versioned for diffable change.

  • Areas index
  • Ownership structures
  • Cost taxonomy

🧬Derived Artifacts

Computed blends persisted for reuse & speed.

  • Price momentum
  • Risk factors
  • Amenity clusters

🧱Template Resources

Parameter-driven resource blueprints.

  • Scenario frames
  • Checklist variants
  • Comparative shells

♻️Refresh Layer

Incremental update orchestration & invalidation.

  • Daily rollups
  • Manual triggers
  • Isolation staging

Operational & Governance Layer

Inspection, safety and iteration mechanisms ensuring controlled evolution rather than prompt drift.

🛰️Observability

Structured logging & tool execution tracing.

  • Tool call envelopes
  • Latency buckets
  • Error taxonomy

🧪Regression Harness

Snapshot + semantic diff tests for prompt outputs.

  • Prompt snapshot
  • Schema validation
  • Delta alerts

🔐Access Control

Capability gating & surface isolation.

  • Role mapping
  • Read scoping
  • Prompt allow-list

🗂️Change Management

Version tagging & rollout staging.

  • Schema bump
  • Shadow deploy
  • Rollback hooks

🪵Drift Detection

Heuristic checks for anomalous output drift.

  • Content signature
  • Variance thresholds
  • Human review queue

⚖️Risk Controls

Guardrails for sensitive or high-impact tools.

  • Rate shaping
  • Input validation
  • Escalation gating

Example Multi-Tool Flow

A composite property acquisition planning call pattern (tool + prompt chaining).

# acquisition_planning composite flow 1. search_properties -> shortlist (scored) 2. get_area_details + area_cost_multipliers for each candidate 3. match_client_preferences (refine order) 4. estimate_closing_costs for top 3 5. ownership_structure_advisor prompt (structures vs goals) 6. investment_opportunity_brief prompt synthesizes: property core + cost bands + area metrics + risk factors 7. due_diligence_brief prompt generates phased checklist

Want a domain-specific MCP server (finance, logistics, compliance) or to extend this pattern? Share scope, data maturity and target workflows - I'll return an initial sequencing plan.

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