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.

Discuss Integration GitHub Tool Principles
Implementation Prompt Layer AI Tools Security Website
  • ▹Schema-first tool contracts
  • ▹Deterministic prompt layer
  • ▹Composable reasoning chains
  • ▹Governed resource namespaces

Tools

30+

Resources

10+

Prompts

5+

Quick Navigation

▹Architecture ▹Tool Design ▹Prompts ▹Security ▹Impact ▹Future

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

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.

Discuss Adaptation Tool Principles Prompt Layer

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.

View Relocation MCP Future Roadmap

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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