AI agents are only as useful as the tools they can reach. If your internal systems are locked behind proprietary interfaces or manual processes, your agents are stuck summarizing documents instead of actually doing work.
The Model Context Protocol (MCP) is the emerging standard for giving AI agents structured, safe access to real tools. We build MCP servers that expose your systems to agents the right way - with proper auth, rate limiting, audit logs, and schemas that don't break when you update your backend.
What you get
- A well-scoped tool catalog - read and write operations defined and separated, with clear descriptions agents can actually use
- Auth and access controls - agents authenticate with your systems the same way your other services do
- Rate limiting and circuit breakers - prevent runaway agents from hammering your APIs or racking up unexpected costs
- Audit logs - a complete record of what every agent called, when, and with what inputs
- Stable versioned schemas - your agents don't break every time your backend changes
How it works
1. Tool catalog design. We map the operations your agents need against your existing systems and define a tool surface that's minimal, clear, and safe. Most systems need fewer tools than you'd expect.
2. Server build. We implement the MCP server with auth, logging, and error handling built in from the start. You get a server that behaves predictably under real agent load.
3. Integration and testing. We connect the server to your agent environment, run it against real scenarios, and make sure it fails gracefully when something goes wrong.
Why this matters
Agents that can't take action aren't worth much. But agents with unconstrained access to your systems are a liability. An MCP server is how you get both - agents that can act, within boundaries you control.
Once your server is live, any agent that supports MCP can use it. That includes Claude, Cursor, and a growing ecosystem of agent frameworks. You build the server once and it works across your stack.
Good fit if
- You want to give AI agents access to internal tools without exposing raw APIs
- You're building an agentic product and need a reliable tool layer under it
- You've tried giving agents API keys and ran into reliability or cost problems