MCP Server Development

What is MCP

Model Context Protocol (MCP) is how AI models talk to your software. It's a standard interface, like USB-C but for AI: one protocol that works across models and tools.

The numbers tell the story. MCP has 97M+ monthly SDK downloads. Anthropic, OpenAI, Google, and Microsoft all support it. It was donated to the Agentic AI Foundation under the Linux Foundation. Claude, Cursor, VS Code, Zed, and Windsurf all use it natively.

Right now, most AI models can't see your data. MCP gives them structured, controlled access to your databases, APIs, and internal tools.

How it works

Your AI (Claude, GPT, Cursor)
        ↕
    MCP Protocol
        ↕
    MCP Server (what we build)
        ↕
Your Systems (Database, API, CRM, Internal Tools)

The MCP server sits between the AI model and your systems. It declares what tools and data are available, handles auth and rate limiting, and logs every interaction.

What we build

We build custom MCP servers for the things off-the-shelf connectors don't cover:

Database connectors that let AI query Postgres, MySQL, MongoDB, or data warehouses with read/write controls
API wrappers that expose your REST or GraphQL endpoints as MCP tools with schema validation
CRM integrations for Salesforce, HubSpot, or custom systems, so AI can look up accounts and update records
Controlled access to admin panels, dashboards, and back-office tools
Document retrieval from knowledge bases, file stores, and internal docs
Multi-tool servers that combine several of these behind unified auth and logging

Who uses MCP

MCP is the default integration protocol for the major AI platforms. Claude supports it natively in Claude Desktop, Claude Code, and the API. Cursor uses MCP servers to extend its coding assistant. GitHub Copilot in VS Code connects to MCP servers for context. OpenAI added MCP support to ChatGPT and the Agents SDK. Zed, Windsurf, and Cline all have MCP integrations.

If you're building with any of these, MCP is how you give them access to your data.

How we work

Scope (1 week)

Map out which systems AI needs to reach, what operations to allow, and what auth and logging you need. We define the tool surface before writing any code.

Build (1-3 weeks)

Develop the MCP server with typed tool definitions, schema validation, error handling, and access controls. We test against real AI clients throughout.

Deploy

Ship with CI/CD, monitoring, and rate limiting. We set up logging so you can see exactly what AI models are doing with your data.

Maintain

Add new tools, update schemas when your APIs change, and tune performance as usage grows. Monthly retainer or on-demand.

Pricing

Engagement	Typical range	Timeline
Single-tool MCP server	$2-5K	1-2 weeks
Multi-tool server with auth	$8-20K	2-4 weeks
Enterprise integration (multi-system)	$20-50K	4-8 weeks
Ongoing maintenance	$2-5K/mo	monthly

FAQ

What is an MCP server?+

It's a server that gives AI models access to your tools, databases, and APIs through the Model Context Protocol. Anthropic created the protocol. OpenAI, Google, and Microsoft all adopted it. The MCP server handles auth, declares what's available, and logs what the model does.

How does MCP work with Claude?+

Claude discovers what tools and data an MCP server offers, then uses them to answer questions or complete tasks with your real data. The server controls what Claude can see and do at every step.

Do I need a custom MCP server?+

Maybe not. Off-the-shelf MCP servers exist for Postgres, Slack, GitHub, and other common tools. But if your data lives in proprietary systems or internal tools, a custom server is the only way to give AI structured access to it.

How much does MCP server development cost?+

A single-tool server with basic auth starts at $2-5K. Multi-tool servers with logging, rate limiting, and production infrastructure run $8-20K. Enterprise integrations across multiple systems range from $20-50K.

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