MCP Server Development

The Model Context Protocol (MCP) is an open standard that lets AI assistants connect to external tools and data sources through one consistent interface. Think of it as a universal adapter between AI agents and your systems.

Introduced by Anthropic in late 2024, MCP has been adopted well beyond its origin — OpenAI, Google, and Microsoft have all announced support, and a broad range of client applications now speak it. Build an MCP server once, and it works across clients such as Claude, ChatGPT, Cursor, VS Code (GitHub Copilot), Windsurf, Cline, and Goose — no per-tool integration code.

Why MCP Matters:

• Standardization — One integration works across the clients that support the protocol
• Security — Controlled, permissioned access to your data and APIs
• Extensibility — Add new capabilities to AI assistants without waiting for vendor support
• Open and versioned — A published, versioned spec rather than a proprietary integration surface you cannot move off

Custom MCP Server Development — I build production-ready MCP servers tailored to your needs. Whether it's connecting AI to your internal APIs, databases, SaaS tools, or custom data sources, I create robust servers with proper error handling, authentication, and documentation.

API Integration — Connect AI assistants to external services like CRMs, project management tools, analytics platforms, or any REST/GraphQL API. I handle authentication flows, rate limiting, and data transformation to ensure reliable integration.

Data Source Connectors — Give AI assistants access to your databases, knowledge bases, or document repositories. I implement secure, efficient querying with proper access controls and caching strategies.

Tool Chain Development — Build sophisticated tool chains that combine multiple operations into cohesive workflows. For example, a server that can search documentation, analyze code, and generate implementation suggestions in a single query.

Open Source Contributions — Need an MCP server for a popular service? I can develop and open source it, benefiting the entire MCP community while meeting your needs.

Most MCP servers return text and hope the model formats it well. That works for a lookup. It falls apart the moment you need to compare, scan, or approve something.

MCP Apps changes that. A tool can declare a View — an interface the AI client renders inline, right where the answer appears. No separate web app to build, host, secure, and log into.

I ship this in production. My YouTube intelligence server exposes 31 tools, seven of which render their results as interactive Views — analytics, search results, channel comparisons, timestamped transcripts — served as ui:// resources, with an automated test suite covering the UI build.

Where it earns its keep:

• Dashboards — comparative data as a sortable table or chart, not a wall of prose you have to re-read every morning
• Approval gates — a proposed write rendered as a reviewable diff with an approve control, so what you approve is the actual payload rather than the model's description of it
• Selection — pick directly from search results, instead of describing in words which of nine items you meant
• Audit — a visible record of what a tool did, in a form a non-technical stakeholder can read

This is genuinely new ground — the extension is only months old, and very few teams have shipped it. If your integration needs to be seen and not just executed, this is the difference between a working prototype and something people trust.

Development Tools:

• GitHub integration for PR reviews, issue management, code search
• GitLab/Bitbucket connectors for self-hosted version control
• CI/CD pipeline integration for build status and deployment info
• Documentation search across internal wikis and knowledge bases

Business Systems:

• CRM integration (Salesforce, HubSpot, custom systems)
• Project management (Jira, Linear, Asana, custom tools)
• Analytics platforms (Google Analytics, Mixpanel, custom dashboards)
• Customer support systems (Zendesk, Intercom, help desks)

Data & Content:

• Database query interfaces with secure access controls
• Content management systems (WordPress, Contentful, custom CMSs)
• Document repositories (SharePoint, Google Drive, internal storage)
• Search engines and knowledge graphs

Specialized Domains:

• Financial data APIs and market information
• Healthcare systems with HIPAA compliance
• Educational platforms and learning management systems
• E-commerce platforms and inventory systems

1. Requirements Gathering — We define the tools, resources, and prompts your MCP server needs to provide. I'll help you think through authentication, permissions, and error handling requirements.

2. Architecture Design — I design the server architecture, choosing between TypeScript or Python implementation based on your needs. For complex integrations, I create a technical design document for review.

3. Implementation — I build the MCP server with clean, maintainable code, comprehensive error handling, and full TypeScript typing (or Python type hints). All servers include logging and debugging capabilities.

4. Testing & Documentation — Thorough testing with real AI clients (Claude Desktop, ChatGPT, Cursor, VS Code, etc.) ensures reliability. Complete documentation covers installation, configuration, and usage examples.

5. Deployment Support — I provide guidance on deployment options (local, npx, Docker) and help integrate the server into your team's workflow.

• Shipped, Not Theoretical — A production MCP server of my own with 31 tools, persistent storage, and seven interactive MCP Apps views, plus substantial contributions to existing open source servers
• Early on the Protocol — 7th place in the oTTomator hackathon (December 2024 – January 2025) with an agent that made MCP tools available to OpenAI-compatible models, built within weeks of the protocol's launch
• Community Moderator for Goose — active in the ecosystem around Block's open source AI agent, not just a consumer of it
• Production Quality — Professional code with proper error handling, logging, and security
• Documentation First — Clear, comprehensive docs that make server adoption easy
• TypeScript & Python — Fluent in both major MCP server languages
• Fast Turnaround — Simple servers in 1-2 weeks, complex integrations in 4-6 weeks
• Open Source Friendly — Happy to open source servers when appropriate