What is Model Context Protocol (MCP)?

An open protocol that standardizes how AI apps connect to tools and data through a client-server model exposing resources and tools.

Is MCP tied to a specific LLM vendor?

No. MCP aims for interoperability so one server can support multiple clients and model providers.

How is security handled?

Security is enforced at the MCP server: authentication, authorization, and auditing of tool calls and resource reads.

Model Context Protocol, done right.

We help teams design, ship, and scale MCP servers and clients—so your AI apps can talk to tools, data, and workflows cleanly and securely.

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Based in Metro Detroit, serving clients nationwide.

Diagram of MCP client ↔ server with tools/resources/prompts

Build reliable MCP servers

Type-safe tooling, sensible auth, solid logging. We turn real systems—databases, file stores, APIs—into clean MCP resources & tools.

Connect to your LLM stack

OpenAI Responses/Agents, Anthropic clients, and more. One server, many consumers—without one-off glue code.

Enterprise patterns

RBAC, PHI/PII handling, audit trails, rate limits, and on‑prem connectivity. Practical, production-first defaults.

What is MCP (in plain English)?

MCP standardizes how AI apps talk to external things—files, databases, APIs, and actions—via a simple client ↔ server protocol. It’s like giving your model a universal port to plug into your stack.

Servers expose resources (readable data) and tools (actions).
Clients (your AI app) request, read, and call them—safely.
You get interoperability instead of custom adapters for each app.

FAQ: quick answers

Is MCP only for one LLM vendor?

No. The goal is interoperability. Build once, connect to multiple clients.

Where does security live?

On the server: auth, permissions, and auditing. Keep secrets close to your systems.