You will get a custom MCP server connecting Claude to your tools and data
Project details
Claude is only as good as the tools it can reach. On its own it can't query your database, call your internal API, or pull from the SaaS your team works in. The Model Context Protocol fixes that, and a clean MCP server is the bridge.
I build custom MCP servers that connect Claude (Claude Desktop, the API, or clients like Cursor) to your real systems, so the model can read your data and take real actions instead of guessing.
What you get:
• An MCP server that wraps your API, database, or tool as typed tools Claude can call
• Proper auth (API key or OAuth), input validation, and clear errors so it does not break on the first edge case
• Tested against real calls, not just a happy-path demo
• A README and config your team can drop in and run
Where these go wrong: tools that return raw blobs Claude chokes on, no auth refresh, no rate limiting, no logging when production breaks. I handle those seams, because that is where integrations actually break.
This is for teams who have a system and want Claude working inside it, not beside it. Not sure it fits? Send a short note on the tool or data you want Claude to use and I will tell you straight before you order.
I build custom MCP servers that connect Claude (Claude Desktop, the API, or clients like Cursor) to your real systems, so the model can read your data and take real actions instead of guessing.
What you get:
• An MCP server that wraps your API, database, or tool as typed tools Claude can call
• Proper auth (API key or OAuth), input validation, and clear errors so it does not break on the first edge case
• Tested against real calls, not just a happy-path demo
• A README and config your team can drop in and run
Where these go wrong: tools that return raw blobs Claude chokes on, no auth refresh, no rate limiting, no logging when production breaks. I handle those seams, because that is where integrations actually break.
This is for teams who have a system and want Claude working inside it, not beside it. Not sure it fits? Send a short note on the tool or data you want Claude to use and I will tell you straight before you order.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$350
|
Standard
$1,200
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | ||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | - |
Setup File | - | - | - |
Source Code |
Frequently asked questions
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FO
Fatih O.
Jun 30, 2026
MCP Server Integration & Voice Assistant MVP Development (1-2 Week Sprint)
Working with Yu has been an absolute pleasure and an incredible journey. We just wrapped up a high-intensity, two-week sprint for a demo project, and the dedication Yu showed was unmatched. Yu is like a marathon runner—extremely fast, highly efficient, and was practically online whenever I logged in, completely shattering any timezone barriers. What I admired most was the obsession with the smallest details, which truly elevated our project to a remarkable level. Even though we only communicated via text, we built a fantastic rhythm and a strong bond of trust. If you are looking for a brilliant developer who genuinely cares about the craft and delivers flawless work under a clock, look no further. I will absolutely be opening a new contract with Yu very soon. Highly, highly recommended!
About Yu Fong
AI Voice Agent Developer | Vapi, Retell, ElevenLabs, Twilio, MCP
Taipei, Taiwan - 3:34 pm local time
Today I run 12 conversational AI agents live in production: they listen, classify intent, answer what's in scope, and hand off to a human the moment they're out of their depth — with memory, dedup, anti-loop guards, and confidence-gated escalation so they don't hallucinate or spam. I've shipped omnichannel support inboxes end to end (Telegram, WhatsApp, Crisp), earned 5★ on my last delivery, and built a production voice assistant in a high-stakes domain (multi-LLM voice pipeline + MCP tool-calling).
Where voice and chat agents actually break isn't the model — it's the seams: latency, flaky function-calling, no clean human handoff, no state recovery after a restart. That's my lane. Stack: Vapi/Retell, Twilio, ElevenLabs/Deepgram, LLM function-calling, FastAPI webhooks, MCP, Python.
What I won't waste your time on: "AI strategy" decks, ChatGPT wrappers you could build yourself, or a demo that was never built to survive real customers. I quote, scope, ship, and hand off documented code with a runbook.
Taipei (GMT+8). Text-first, fluent written English, same-day replies.
Steps for completing your project
After purchasing the project, send requirements so Yu Fong can start the project.
Delivery time starts when Yu Fong receives requirements from you.
Yu Fong works on your project following the steps below.
Revisions may occur after the delivery date.
Scope your tools and data
I map which of your APIs, data, or tools Claude should reach, and how.
Build the MCP server
I wrap each tool as a typed MCP tool with auth, input validation, and clear errors.