Thailand Relocation MCP Server

Access / Integration: If you want trial or integration access to the Thailand Relocation MCP (or to adapt the pattern for another destination), reach out via the contact form with a brief use case and intended channel (web, chat, internal agent). I can provision limited API / SSE credentials or a scoped prompt + tool set for evaluation.

The Thailand Relocation MCP Server converts a deep, structured knowledge base about moving and settling in Thailand into a uniform surface of AI-callable tools, resources, and prompts. Originally scoped for Dutch expats, it now serves a broader international audience planning long‑, mid‑ or exploratory stays.

🛠️Operational Use Beyond Q&A

This MCP isn’t just a question-answer backend. It actively participates in daily content maintenance, regulatory refresh cycles, assisted drafting of new blog posts & guide updates, and structured change propagation across multiple surfaces (web + chat + internal planning tools).

Daily sync jobs pull vetted news, policy & visa rule shifts into staging JSON (human review before publish).
Regulation diffs trigger regeneration of affected prompts & resource snapshots (e.g. fee bands, document checklists).
Bulk content sweep tools flag stale sections (age heuristics + external signal deltas) for one‑click refresh.
Blog & guide drafting: prompt templates invoke domain tools to assemble updated context packs before editorial polish.
Consistency layer: one data source → multi-channel output (web pages, Telegram bot, internal planning briefs) with zero manual duplication.

Net effect: authoritative relocation intelligence stays current with hours not weeks of lag, and large-scale edits propagate reliably without copy/paste risk.

Current Deployment

Live usage today powers:

Public multilingual assistant at settleinthailand.com
Original Dutch-focused experience at nl2thailand.com
Open Telegram bot for quick trials: @phad_ai_en_bot

The same MCP surface feeds these channels, enabling consistent structured reasoning (visa routes, budgeting, housing pathways, healthcare navigation) across web and chat surfaces without duplicating logic.

Integrated Real Estate Layer

This server supersedes and folds in the full real estate MCP capability set (search, comparable sizing, closing cost estimation, ownership structure guidance, due diligence checklists). It’s adapted for the Thai condo–first reality of overseas / expat acquisition (foreign quota considerations, financing pathways, risk advisories). Agent-facing tools (matching, quick plan scaffolds) and buyer guidance prompts now run on the same unified relocation data manager—so a budgeting or visa prompt can seamlessly chain into property discovery, and property context can enrich relocation or retirement planning outputs. Houses / land flows remain intentionally minimal; focus is high-quality condo intelligence and compliant ownership pathways.

New Education Domain (Aug 2025)

Added a structured education dataset powering school discovery & planning for relocating families:

International & bilingual schools (curriculum, phases, fee bands, strengths)
Alternative / homeschooling & online pathways
Early childhood models (nursery, Montessori, Reggio, fee ranges)
Higher education pathways & bridge programs
SEN (Special Educational Needs) support centers & service taxonomy

Tools expose concise, machine-composable slices (list / detail / aggregated summary) and stable resources under thailand://education/*. This lets a family relocation prompt stitch visa timing + budget + area + schooling fit in one chain without token bloat.

What It Does (In One Breath)

Visa routes, budget estimation, area intelligence, rentals vs. purchase planning, healthcare navigation, cultural adaptation, retirement pathways—all exposed as typed MCP tools and stable thailand:// resources the model can chain together for context-rich answers.

Why It’s Useful

Replaces sprawling forum scraping + ad‑hoc Googling with canonical, versioned data
Tools return structured JSON the model can reason over (not prose blobs)
Prompt templates guide multi‑step reasoning: visa strategy, budget scenarios, rental negotiation, holistic relocation plan
SSE transport = easy inspection (MCP Inspector, curl) + multi-client access

Surface Snapshot

20+ tools (e.g. estimate_budget, get_visa_route, rental_quick_plan, estimate_closing_costs, get_area_cost_snapshot)
10+ prompt templates (visa strategy, budgeting, housing, healthcare, culture, composite relocation)
Stable resources like thailand://visa/types, thailand://areas, thailand://realestate/ownership, new education endpoints (thailand://education/schools, thailand://education/alternative, etc.), plus parameterized templates (thailand://budget/estimate/{city}/{household}/{level})

Tool Catalog (Full List)

All tool identifiers exposed (logic intentionally summarized to avoid giving away proprietary composition patterns). Return types are structured JSON (never plain prose) for downstream prompt-chaining.

Visa & Legal

get_visa_types – Catalog of visa types
list_visa_routes – High-level route metadata
get_visa_route – Detailed sequence / checkpoints (optional brief mode)
get_visa_checklist – Required documents & timing (per type)

Banking & Finance

get_banking_accounts – Account archetypes & use cases
get_transfer_methods – FX / remittance pathways
get_banking_tax_notes – Selected tax / compliance considerations

Budget & Cost of Living

estimate_budget – City + household profile estimator (multi-band)
compare_cities_multi – Cross‑city cost component comparison
get_budget_profiles – Household archetype keys
get_city_multipliers – Category cost multipliers (ratio form)

Rentals

rental_quick_plan – Compressed acquisition & setup plan
compare_rental_structures – Lease / structure pros & risks
get_rental_process – Phase breakdown (by property type)
get_rental_costs – Upfront + recurring components
get_rental_risks – Risk advisories & mitigations

Real Estate Purchase

estimate_closing_costs – Transaction cost composition estimator
property_buying_quick_plan – Condensed acquisition timeline & due diligence focus points
get_ownership_rules – Foreign ownership frameworks
get_realestate_structures – Acquisition / holding structures
get_due_diligence_checklist – Checklist per property type

Areas & Local Intelligence

get_all_areas – Master area index
get_area_cost_snapshot – Cost multipliers + lifestyle tags
get_area_details – Full descriptor & amenities (parameterized)

Cultural & Language Adaptation

get_culture_topics – Etiquette & adaptation domains
get_language_phrases – Phrase pack by topic

Healthcare

get_healthcare_overview – System structure & access pathways
get_hospitals_by_city – Hospital listing + attributes filter

Education

list_schools – International & bilingual school catalog (filtered server-side)
get_school – Detailed school profile by slug
summarize_school_landscape – Aggregated multi-city / area comparison snapshot
list_alternative_education – Homeschooling & alternative models overview
list_early_childhood_models – Early childhood care / pedagogy models & indicative fees
list_higher_ed_pathways – Higher education program pathways
list_sen_support_services – SEN support categories & centers

Guides & Knowledge Base

list_guides – Structured guide index
get_guide – Guide by slug
search_knowledge – Cross-domain keyword semantic search

Retirement Planning

list_retirement_visas – Retirement pathways overview
retirement_plan – Composite retirement scenario snapshot

Relocation Composite / System

relocation_checklist – High-level phased task list
get_persona_profile – Relocation persona profile (scenario scaffolding)
system_summary – Dataset + tool counts / diagnostics
refresh_cache – Reload immutable JSON (dev / maintenance)

Prompt Catalog (Template Identifiers)

Prompt templates are pure string generators with structured section scaffolds. They orchestrate multi-tool flows; internal heuristics redacted.

Visa Strategy

visa_strategy_overview – Primary vs alternative routes
visa_route_comparison – Multi-route risk & requirement matrix

Banking & FX

banking_setup_plan – Multi-account onboarding & remittance plan
fx_optimization_brief – Transfer & timing guidance

Budget & Financial Planning

budget_scenario_analysis – Toggle lifestyle / age / insurance bands
multi_city_budget_compare – Ranked differential summary

Rentals & Housing

rental_acquisition_plan – Search → negotiation → move‑in framing
rental_negotiation_brief – Concession & counter strategy
housing_pathways_matrix – Rent vs buy vs hybrid framing

Real Estate Risk & Due Diligence

realestate_due_diligence_plan – Sequenced verification list
ownership_structure_advisor – Structure suitability scoring

Area & Suitability

area_comparison_scorecard – Weighted factor summary
area_fit_assessment – Persona suitability narrative

healthcare_navigation_brief – Access, insurance, facility strategy
hospital_selection_prompt – Facility comparison criteria

Culture & Language

culture_adaptation_sprint – 30‑day focus plan
language_phrase_retention_plan – Spaced usage strategy

Retirement & Long-Term

retirement_strategy_outline – Visa + cost + healthcare pillars

Holistic Relocation

relocation_composite_plan – End‑to‑end phased blueprint

IP Note: Template internals (section scoring formulas, weighting heuristics, conditional inserts) intentionally not published; identifiers provided for transparency and integration clarity.

Prompt Architecture & Design Philosophy

The relocation prompt layer turns raw JSON outputs into structured, reasoning-friendly briefs the model can extend without hallucinating or drifting. Core principles:

1. Pure + Deterministic
Every prompt function is a pure template: no network calls, no time-based randomness. This guarantees reproducible shaping of tool outputs and makes test diffs meaningful.

2. Section Contracts
Each template follows a consistent scaffold (HEADER → CONTEXT SUMMARY → DATA EXTRACTS → ANALYSIS FRAMES → ACTION / NEXT STEPS). Downstream chains can target sections by heading token instead of brittle regex over free text.

3. Data Fusion Not Dumping
Prompts never paste full raw tool payloads; they compose a selective, labeled subset (e.g., top 5 cost drivers, 3 highest‑variance line items, visa gating constraints) reducing token load while keeping semantic anchors.

4. Pre-Normalized Vocabulary
Domain synonyms are collapsed earlier in the data layer (e.g., “condo quota” vs “foreign ownership allocation”) so prompts use a single canonical phrase; improves follow‑up instruction precision.

5. Lightweight Scoring Hints (Redacted Logic)
Score bands (e.g., suitability 0–5, risk low/med/high) are rendered with uniform labels; internal weighting math remains private but the output shape is stable for UI or agent consumption.

6. Chainability & Idempotence
Re‑invoking a prompt with identical arguments yields identical structured sections; subsequent enrichment steps (adding education or real estate context) append new labeled blocks instead of rewriting earlier analysis.

Prompt Categories (Deepened)

Category	Input Focus	Output Shape	Typical Follow-On Tool Calls
Visa Strategy	visa_type(s), profile flags	Route matrix + gating factors	checklist fetch, timeline calc
Budget Scenario	city, household, lifestyle level	Normalized monthly band table + sensitivity notes	area snapshot, rental quick plan
Housing / Rentals	city, property_type, budget	Acquisition phases + negotiation leverage list	rental_costs, area_cost_snapshot
Real Estate Due Diligence	property_type, structure	Risk cluster summary + due diligence checklist header	due_diligence resource, closing_costs estimator
Area Comparison	areas[], persona traits	Weighted scorecard + trade‑off narrative	area_details, budget estimate
Healthcare Navigation	city, insurance posture	Access pathway + facility shortlist criteria	hospitals_by_city
Culture / Language	topic set, timeframe	30‑day sprint schedule + phrase focus	language_phrases
Education (New)	city/area set, curriculum prefs	School landscape summary + shortlist rationale	list_schools, summarize_school_landscape
Holistic Relocation	persona, time horizon	Phased integrated plan (visa→housing→schooling→budget)	any sub-domain tool

Example (Simplified) Pattern

Pseudo-structure (abbreviated to avoid leaking internal heuristics):

RELOCATION PLAN (persona: remote family, horizon: 12m)
1. VISA STRATEGY
  - Primary: Non-O (family) → extension path summary
  - Alt: ED route (if schooling pivot) …
2. BUDGET SNAPSHOT (Bangkok, mid, 2 adults + 1 child)
  - Core monthly THB bands: housing X – Y, education …
3. AREA SHORTLIST (filters: intl school proximity, transit)
  - Ranked: AreaA (score 4.4), AreaB (4.1)… key trade-offs
4. HOUSING PATHWAY (rent-first → evaluate purchase at month 9)
5. EDUCATION OPTIONS (curriculum preference: IB)
6. RISK / WATCHLIST (regulatory, timing, quota)
7. NEXT ACTION BLOCK (week 1 tasks)

Validation & Testing Approach

Snapshot tests assert stable section ordering / headings
Token count guardrails (alert if expansion > threshold)
Lint pass ensures no raw JSON blocks exceed allowed line count

Why This Matters

Well-behaved prompts shrink model deliberation overhead, reduce hallucinated entities, and create a reliable substrate for autonomous multi-step agents (e.g., “refine budget then regenerate housing plan if rent > threshold”). The structured, minimal style accelerates adaptation for new domains (education just plugged into Holistic Relocation without touching prior sections).

Future: introduce delta prompts (only emit changes since previous plan revision) to further cut token usage in iterative workflows.

🧭Example Query → Tool / Prompt Flows

User Query: “What’s a realistic monthly budget for a solo remote worker in Bangkok, mid lifestyle, and which areas should I shortlist?”
Flow:

Tool estimate_budget (args: city=Bangkok, household=solo, level=mid, brief=true)
Tool get_area_cost_snapshot (for top candidate areas derived from budget’s housing band logic)
Prompt budget_scenario_analysis merges normalized monthly band + variance notes
Prompt area_comparison_scorecard composes weighted suitability (cost, transit, lifestyle, school distance)

Response Shape: Budget table (core categories + ranges) + ranked area scorecard with trade‑offs + next action list.

User Query: “Outline my path to a long‑term family relocation: visa, schooling, interim rent, eventual condo purchase.”
Flow:

Tool get_visa_route (primary family visa code)
Tool list_schools (filters: curriculum=IB, city=Bangkok)
Tool rental_quick_plan (property_type=condo, budget_thb=specified)
Tool estimate_closing_costs (property_type=condo, property_value_thb)
Prompt relocation_composite_plan stitches visa timeline + schooling window + rent-first housing path + purchase evaluation milestone (month 9) + budgeting deltas

Response Shape: Phased plan (sections: Visa Strategy, Education Window, Rental Acquisition, Purchase Evaluation, Cost Anchors, Risks / Watchlist, Immediate Tasks).

User Query: “Compare Non-O family visa vs ED visa trade‑offs for my situation.”
Flow:

Tool get_visa_route (code=Non-O)
Tool get_visa_route (code=ED)
Prompt visa_route_comparison generates matrix (requirements, renewal friction, cost, risk flags)

Response Shape: Side-by-side matrix + narrative recommendation + decision trigger points.

User Query: “Give me a concise view of international school options near my budget and area preferences.”
Flow:

Tool list_schools (filters: curriculum list + fee band)
Tool summarize_school_landscape (area subset)
Prompt area_comparison_scorecard (optional) to overlay area suitability
Prompt relocation_composite_plan (optional) to integrate into overall plan revision

Response Shape: Curated shortlist (max 5) + curriculum rationale + area alignment notes + next enrollment timing steps.

User Query: “Estimate closing costs for a 5M THB condo and highlight major risk checks.”
Flow:

Tool estimate_closing_costs
Tool get_due_diligence_checklist (property_type=condo)
Prompt realestate_due_diligence_plan merges cost composition + risk clusters + verification sequence

Response Shape: Cost breakdown JSON → summarized table + prioritized due diligence tasks + red flag watchlist.

(All flows omit private weighting heuristics; sequence shows external contract only.)

Design Highlights

Single data manager loads immutable JSON once (fast, deterministic)
Domains separated by tool/resource/ prompt modules (easy extension)
Pure prompts (no I/O) simplify testing & reproducibility
Structured calculators (budget, closing costs) embedded—no external calls needed

Example Calls

# Budget snapshot (solo, Bangkok, mid)
curl -X POST http://127.0.0.1:8000/messages/ \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"estimate_budget","arguments":{"city":"Bangkok","household":"solo","level":"mid","brief":true}}}'
 
# Visa route (tourist TR-60)
curl -X POST http://127.0.0.1:8000/messages/ \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"get_visa_route","arguments":{"code":"TR-60","brief":true}}}'

Emerging Pattern

Like the broader MCP approach I outlined in the core post, this server shows how domain-normalized data + semantic tooling + guided prompts transform relocation help from generic advice into adaptive, profile-aware planning.

Road Ahead

Adding housing developer/project catalogs, richer comparative area scoring, and multi‑city scenario planners—without changing the contract surface.

Want to experiment or adapt the pattern for another country? Let’s talk.

Read the MCP Servers Deep Dive → | Contact →

Edwin

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