MCP Server Creation & Data Curation Layer
I build a focused MCP server plus the data engine behind it. Clean structured data + small purposeful tools + tests that catch drift early = answers you can trust.
Objective: Create a domain intelligence layer that delivers structured, reliable tool outputs - not transient prompt experiments.
Schema First
Entities, relationships & query modes mapped before tool code.
Evaluation Wired
Representative regression sets & scoring harness from week 1.
Lifecycle Automation
Ingestion → normalize → re‑index → drift alert loops scheduled.
Observability
Latency, cost, retrieval stats, eval deltas & tool usage metrics.
Common Failure Patterns
Many AI builds fail because they start with prompts, ignore data structure, never test quality, and let content go stale. I flip that: structure first, tools second, tests always.
Prompts change, quality drifts & no one notices.
Knowledge corpus goes stale; no refresh policy.
Generic chat interface cannot execute structured tasks.
Hard to justify LLM spend without instrumentation.
From Mapping → Lifecycle
Ship a minimal evaluable spine, then layer tools, evaluation depth & automation based on observed usage - not assumption.
Principle
No phase expands until the previous produces stable signals: retrieval quality, tool latency, evaluation trend & dataset freshness.
Phase 1 – Map Domain
List key things, sources & what 'good' looks like.
Phase 2 – Seed Spine
Schema + first clean data + tiny search tool + simple tests.
Phase 3 – Add Tools
Compare, classify, plan + smarter retrieval + logging.
Phase 4 – Test & Guard
Score answers, watch drift, track cost & speed.
Phase 5 – Automate
Scheduled refresh, re-index, deploy & metrics.
Phase 6 – Optimize
Add advanced tools, grow data, tune cost & latency.
What Makes It Durable
Governed Dataset Workflow
I follow a clear cycle (plan → collect → clean → test → refresh). The full methodology shows how I keep data trusted, versioned and up to date.
Why it matters
Prevents drift, enables auditability, supports evaluation accuracy & accelerates safe expansion of tool surfaces over time.
- ▹ Source-backed over speculative synthesis
- ▹ Schema before ingestion to prevent uncontrolled sprawl
- ▹ Small evaluable slices > monolithic dump
- ▹ Changelog & version semantics for every structural change
- ▹ Layered freshness probes (daily / weekly / event-triggered)
- ▹ Explicit uncertainty flags instead of silent assumptions
- ▹ Separation of raw capture, normalized JSON, and tool contract views
Unbranded Public References
Below are open narrative pages illustrating architecture, dataset strategy & tool design patterns similar to what we deploy (adapted per domain). They are examples - not templates.
Real Estate MCP Detail
Acquisition & ownership intelligence: structures, costs, due diligence tools.
View reference →Thailand Relocation MCP Detail
Unified relocation domain: visa routes, budgeting, housing & planning resources.
View reference →Data Methodology & Curation
Structured acquisition, versioning & refresh workflow behind dataset quality.
View reference →Representative Patterns
Ops Decision Support
Surface normalized internal + external signals for faster triage / planning.
Research & Monitoring
Continuously updated vertical dataset powering search + compare tools.
Compliance / Policy Layer
Structured queries & classification over evolving regulatory corpus.
Market Intelligence
Pricing / listing / trend ingestion with enrichment & scoring tools.
Ready to map a domain spine?
We’ll scope entities, signals & first tools in a short alignment and ship a working spine fast - then iterate with evidence.