I build custom MCP servers that connect AI apps and agents to CRMs, SaaS platforms, internal APIs, dashboards, databases, spreadsheets, documents, and workflow tools.

This is useful for teams building with OpenAI, ChatGPT apps, Claude, Cursor, Claude Code, custom LLM apps, or internal AI agents that need controlled access to business data and actions.

With a custom MCP server, your AI assistant can safely call structured tools to search records, pull reports, check customer data, update fields, create tasks, process files, query databases, or trigger approved workflows.

For the first version, I focus on a practical MVP that can be tested quickly in 2–5 days. Instead of building a huge connector immediately, I help you prove the most valuable workflow first, then expand from there.

Working MCP server in TypeScript or Go

Tool definitions for selected workflows

API, SaaS, CRM, database, or file integration

Input validation and safe error handling

Authentication setup

Basic logging and debugging structure

Local or remote setup guidance

Written documentation

Short handoff walkthrough

AI workflow automation

OpenAI or ChatGPT app tooling

Claude and Cursor tool integration

Internal AI agents

CRM automation

SaaS connectors

Database access tools

Internal API wrappers

Backend AI infrastructure

Business operations automation

Vague “build me an AI agent” requests with no clear workflow, no API access, no sample data, or no permission to connect to the target system.

For a 2–5 day market-test build, I recommend starting with 3–7 useful MCP tools. This keeps the server focused, testable, and easier for your team to maintain.