You will get a production-grade MCP server connecting your API to Claude
Project details
You’ll get a production-ready MCP server that enables Claude (or any LLM agent) to interact with your API reliably, securely, and at scale.
I build robust MCP servers that sit between your AI agents and backend systems, providing strict tool schemas, comprehensive input validation, rate-limit-aware request handling, intelligent error classification, and complete observability through logging and monitoring.
My implementations are already powering CRM platforms, payment systems, and custom internal APIs, reducing response times from 60+ seconds to under 5 seconds while eliminating reliability issues in production environments.
The deliverable includes clean, maintainable TypeScript source code, deployment instructions, comprehensive documentation, and end-to-end testing against your live API.
You bring the API. I handle the engineering.
I build robust MCP servers that sit between your AI agents and backend systems, providing strict tool schemas, comprehensive input validation, rate-limit-aware request handling, intelligent error classification, and complete observability through logging and monitoring.
My implementations are already powering CRM platforms, payment systems, and custom internal APIs, reducing response times from 60+ seconds to under 5 seconds while eliminating reliability issues in production environments.
The deliverable includes clean, maintainable TypeScript source code, deployment instructions, comprehensive documentation, and end-to-end testing against your live API.
You bring the API. I handle the engineering.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AIOps, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging FaceAI Models
ChatGPT, GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$4,000
|
Advanced
$8,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 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 |
Optional add-ons
You can add these on the next page.
Additional API connector
(+ 5 Days)
+$1,500
30 days post-launch support
+$500Frequently asked questions
About Jakub
AI Automation Engineer | MCP Servers, AI Agents & Data Pipelines
Wroclaw, Poland - 1:57 am local time
Most automation breaks because it's glued together: brittle no-code chains, API calls with no error handling, "solutions" that work in a demo and fall over under real load. I build the opposite: systems with validation, rate limiting, logging, and monitoring, so they hold up when it actually matters.
WHAT I BUILD
- AI agents & custom MCP servers
Connect Claude or GPT to your CRM, payments, and internal tools: type-safe, rate-limited, and fully logged. Your agent gets reliable, structured access instead of fragile prompt-based API calls.
- Data pipelines & integrations
Join CRM, product, support, and payment data into one clean source of truth. CRM/ERP/MRP integrations that eliminate manual data entry, plus offline conversion attribution that restores lost tracking for paid campaigns.
- B2B outbound infrastructure
Apollo + scraping + enrichment + email sequencing, built as a system: trigger-event detection (new hires, funding, tech changes) feeding signal-based outreach. Not a one-off list, but a pipeline that runs.
- Workflow automation
End-to-end automation on Make, n8n, Airtable, and Zapier, from intake to delivery, with a global error handler so nothing fails silently.
HOW I WORK
I own the full cycle: business analysis, process mapping, build, testing against your live systems, and performance monitoring after launch. I scope the work before I build it, so you know exactly what you're getting and what it will do.
For complex work I go well beyond no-code: Python, Node.js, TypeScript, Next.js, PostgreSQL, and SQL. My AI stack includes Anthropic Claude, OpenAI, LangChain, and Vertex AI, deployed on Railway, Hetzner, or GCP.
RECENT RESULTS
- Turned 60-second, error-prone API calls into 5-second systems with zero failures in production (uptime 86% to 99.5%)
- Cut prospect research from 10 minutes to 2 per lead
- Eliminated manual data entry across CRM, support, and payments
- Built outbound pipelines processing thousands of qualified leads per week
WHO I WORK WITH
B2B SaaS companies, agencies, and teams whose sales or operations people are drowning in manual work, especially those who want AI integrated properly into the tools they already use, not bolted on as a gimmick.
I handle projects where engineering quality matters: things that run in production, touch real customer data, and can't quietly break. If that sounds like the level you need, we'll work well together.
HOW TO START
Send me a short note about the system or process you want to automate, the tools involved, and what "done" looks like for you. I'll tell you honestly whether it's a fit, how I'd approach it, and what to expect.
I lead a 7-person automation team in my day role, so I'm selective with freelance work and take on projects where it counts. If you want AI agents, integrations, or outbound systems built properly and built to last, let's talk.
Steps for completing your project
After purchasing the project, send requirements so Jakub can start the project.
Delivery time starts when Jakub receives requirements from you.
Jakub works on your project following the steps below.
Revisions may occur after the delivery date.
Scope & tool design
I review your API docs and requirements, then define the exact tools Claude will use and the data each returns. You confirm the scope before I build anything.
Build the MCP server
I build the server in TypeScript: tool schema, input validation, rate limiting, token/OAuth handling, and error classification. Clean, documented, production-grade code.