You will get Custom MCP Server Development for OpenAI, Claude, APIs, and SaaS Tools
Project details
Most teams using AI assistants eventually hit the same wall: the model is useful in conversation, but it cannot safely work with the real systems where the business runs.
Your workflow may depend on a CRM, internal API, private dashboard, niche SaaS platform, database, spreadsheet, document store, or automation system with no ready connector. I build custom MCP servers that close that gap.
With a custom MCP server, your AI assistant can call structured tools to search records, pull customer data, check pipeline status, create tasks, update fields, process files, fetch reports, query databases, or trigger approved workflows.
This is useful for teams building with OpenAI, ChatGPT apps, Claude, Cursor, Claude Code, custom LLM apps, or internal AI agents.
For a fast MVP, I usually recommend starting with 3–7 high-value tools first. This keeps the project practical for a 2–5 day delivery and helps prove the workflow before expanding into a larger connector.
Your workflow may depend on a CRM, internal API, private dashboard, niche SaaS platform, database, spreadsheet, document store, or automation system with no ready connector. I build custom MCP servers that close that gap.
With a custom MCP server, your AI assistant can call structured tools to search records, pull customer data, check pipeline status, create tasks, update fields, process files, fetch reports, query databases, or trigger approved workflows.
This is useful for teams building with OpenAI, ChatGPT apps, Claude, Cursor, Claude Code, custom LLM apps, or internal AI agents.
For a fast MVP, I usually recommend starting with 3–7 high-value tools first. This keeps the project practical for a 2–5 day delivery and helps prove the workflow before expanding into a larger connector.
AI Algorithms
Convolutional Neural Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI Text-to-Image, AI Text-to-Speech, Conversational AI, Image Processing, Image Recognition, Image-to-Image Translation, Natural Language GenerationAI Tools
Azure OpenAI, Hugging Face, NVIDIA AI Platform, Replit, TensorFlowAI Models
ChatGPT, DALL-E, GPT-4, LLaMA, Midjourney AI, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$200
|
Standard
$650
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 8 days |
Number of Revisions | 10 | 10 | 10 |
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 |
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MM
Mike M.
Jun 24, 2026
E-Mail function on an established website
It is a pleasure to do business with him. He is a true professional, and I will continue to use his services again.
About Muh
AI Automation & Backend Systems | Scraping, LLMs, APIs, RAG, TS/Go
100%
Job Success
Sokaraja, Indonesia - 3:52 pm local time
My work usually combines three parts:
1. Data collection — scraping, API-first extraction, web automation, document ingestion, and external data integration.
2. AI processing — LLM-based cleanup, classification, summarization, deduplication, enrichment, structured extraction, and reasoning over messy data.
3. Retrieval systems — RAG pipelines for searching, indexing, and answering questions from documents, scraped content, product catalogs, internal knowledge bases, or business records.
I work mainly with TypeScript, Node.js, Go, and Python when needed. My usual approach is API-first: inspect the website or system, find the most stable data path, then use browser automation only when necessary. For AI systems, I focus on practical backend workflows, not just prompt demos.
Relevant Upwork work includes KTB Automation API, Krungthai Business Bank Middleware Automation, and a recent 5-star website/email function project where the client described me as “a true professional” and said they would continue using my services.
Good fit for:
AI-powered web scraping and data extraction
RAG systems for documents, websites, or internal knowledge bases
LLM processing for scraped or messy data
Product, marketplace, real estate, lead, or research data pipelines
Chatbots connected to custom data sources
API-first extraction from websites with hidden or internal endpoints
Scheduled scraping jobs with CSV, Excel, Google Sheets, Airtable, or database output
Backend automation using Go, Node.js/Bun with TypeScript or Python.
I prefer practical systems over AI demos. The goal is clean data, reliable automation, useful retrieval, and a workflow that can be reused.
Send the target website, documents, required fields, output format, and whether this is one-time or recurring. I’ll tell you clearly whether I’m the right fit.
Steps for completing your project
After purchasing the project, send requirements so Muh can start the project.
Delivery time starts when Muh receives requirements from you.
Muh works on your project following the steps below.
Revisions may occur after the delivery date.
Review workflow and access requirements
I review your target workflow, API documentation, sample data, authentication method, and which actions the AI assistant should be allowed to perform.
Define MCP tools and scope
I convert the workflow into a focused MCP tool list, usually 2–10 tools depending on the package, with clear inputs, outputs, and permission boundaries.