You will get MCP servers setup in Claude, Cursor, Windsurf, Copilot & any other client
Project details
You will get a fully working MCP setup in Claude Desktop, Cursor, Windsurf, or VS Code — configured, tested, and documented.
MCP (Model Context Protocol) connects AI clients to external tools like GitHub, Linear, databases, and custom APIs. But getting it to actually work — right paths, right config format, right permissions — is surprisingly painful.
I've been running MCP in production for months across multiple clients and servers. I know exactly where it breaks and how to fix it fast.
What I do:
– Review your current config and environment
– Fix JSON/YAML errors, path issues, missing env vars
– Resolve server startup failures and tool registration bugs
– Test live connection between client and server
– Deliver a documented setup summary so you can maintain it yourself
I work async and communicate clearly. You'll know what was wrong and why the fix works.
MCP (Model Context Protocol) connects AI clients to external tools like GitHub, Linear, databases, and custom APIs. But getting it to actually work — right paths, right config format, right permissions — is surprisingly painful.
I've been running MCP in production for months across multiple clients and servers. I know exactly where it breaks and how to fix it fast.
What I do:
– Review your current config and environment
– Fix JSON/YAML errors, path issues, missing env vars
– Resolve server startup failures and tool registration bugs
– Test live connection between client and server
– Deliver a documented setup summary so you can maintain it yourself
I work async and communicate clearly. You'll know what was wrong and why the fix works.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI-Generated Code, Conversational AI, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Tools
Azure OpenAI, Bing AI, GitHub CopilotAI Models
ChatGPT, LLaMAWhat's included
| Service Tiers |
Starter
$25
|
Standard
$35
|
Advanced
$55
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 1 | 2 |
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 | - | - | - |
About Mikhail
Agentic AI Engineer | MCP | LangGraph | LLM Infrastructure
Tel Aviv, Israel - 5:36 am local time
10+ years in production engineering. Led a platform team from 1 to 13 engineers. Shipped products to 5M+ users while keeping 99.5–100% crash-free rate in production.
My flagship project is CyberMem — a self-hosted MCP memory platform I designed and shipped end-to-end, used by Claude, GPT, Cursor, Gemini, and Perplexity. It runs on Docker, Kubernetes, and Raspberry Pi with Prometheus/Grafana observability, Traefik zero-trust auth, automated versioned releases via GitHub Actions, and Vitest test suite. 550+ commits, 25 versioned releases. Live at cybermem.dev.
I also built EasyOref: a LangGraph-based multi-step alert agent with MCP tool use, BullMQ queues, RAG enrichment, and Telegram delivery — running live in production.
What I work on:
- MCP server development (memory, API bridges, file gateways, custom tools) — Docker/K8s deployed, schema-driven, with auth, observability, and docs
- LangGraph / LangChain workflows — stateful agents with tool use, conditional branching, human-in-the-loop, rollback
- RAG pipelines — document ingestion, embeddings, vector store, retrieval tuning, clean Q&A API
- MCP client setup — Claude Desktop, Cursor, Windsurf, Perplexity — wired correctly with working auth
- Architecture reviews — for teams building agent systems before committing to a design
Positioning: AI Infra Engineer who ships production systems, not demos.
Stack: TypeScript/Node.js, Python, LangGraph, LangChain, FastMCP, Docker, Kubernetes, Redis, BullMQ, Prometheus, Vitest, GitHub Actions.
Send me a short message with your use case, and I'll tell you the fastest way to ship it.
Portfolio: mikhailkogan.dev
Steps for completing your project
After purchasing the project, send requirements so Mikhail can start the project.
Delivery time starts when Mikhail receives requirements from you.
Mikhail works on your project following the steps below.
Revisions may occur after the delivery date.
Diagnose your setup
Review your config, identify connection issues and missing dependencies
Fix config & test live
Fix configuration files, install missing dependencies, verify live MCP connection