AI agents are only as useful as the tools you give them access to. We build custom MCP (Model Context Protocol) servers that expose your SaaS, internal APIs, or databases to Claude, ChatGPT and other agents — with the authentication and access boundaries a production system actually needs, not a demo-grade wrapper.
MCP is new enough that most implementations are copy-pasted from a quickstart. A dedicated team builds the version that survives real usage and real security review.
Proper error handling, rate limiting, and observability — so an agent calling your tools doesn't take down the same systems your human users depend on.
OAuth, scoped API keys, and per-user permission boundaries so an agent only ever sees and does what the calling user is allowed to.
Hosted MCP servers reachable over HTTP for Claude, ChatGPT and other agent platforms, or local stdio servers for internal tooling — scoped to what you actually need.
Tools, resources, and prompts modeled around what an agent needs to reason well — not a raw pass-through of your existing REST API.
We treat every MCP server as a new attack surface — input validation, audit logging, and least-privilege access to your data layer from day one.
MCP is evolving fast. The people building your server follow the protocol changes directly instead of working off a stale tutorial.
Scoped to the agents and workflows you actually want to support — not a generic integration package.
A purpose-built server exposing your product's core actions and data as tools and resources for AI agents.
Turning existing internal APIs or databases into agent-accessible tools without duplicating business logic.
Deployed, authenticated MCP servers reachable by Claude, ChatGPT and other agent clients over the network.
Wiring your MCP server into Claude Desktop, Claude Code, ChatGPT connectors, or a custom agent runtime.
MCP servers designed to work cleanly inside larger agent workflows built with LangGraph or similar frameworks.
Access control, rate limiting, and audit logging review for MCP servers already in production or close to launch.
An MCP (Model Context Protocol) server is a standardized way to expose your product's data and actions as tools an AI agent — like Claude or ChatGPT — can call directly. If you want customers or internal teams to use AI agents against your product safely, an MCP server is the supported way to do it instead of ad-hoc API keys pasted into a prompt.
A focused server exposing a handful of well-defined tools against an existing API can usually ship in a few weeks. Timelines grow with the number of tools, the complexity of the auth model, and whether we're also wrapping legacy or undocumented internal systems.
Yes, that's the core use case — but it needs to be built with the same access control discipline as any other API surface. We scope tools narrowly, enforce per-user permissions, and log every call so agent access doesn't become a shortcut around your existing security model.
A REST API is designed for a developer to read documentation and write code against it. An MCP server is designed for an AI agent to discover and call at runtime — it describes its own tools, inputs, and outputs in a way the agent can reason about, which a typical REST API doesn't do.
Yes. We build both local (stdio) servers for internal or desktop use and remote, hosted MCP servers reachable over HTTP with OAuth or scoped API key authentication — the setup most SaaS products need to support external agent clients.
Tell us what you want AI agents to be able to do in your product — we'll scope the tools, auth model, and a fixed estimate.