MXCP

Overview

MXCP is an enterprise‑grade framework that turns a collection of data models, business logic, and security policies into a fully‑featured Model Context Protocol (MCP) server. Instead of wiring up ad‑hoc APIs, developers can describe their data contracts with dbt, declare tool signatures and RBAC rules in a single configuration file, and let MXCP generate a production‑ready MCP endpoint that enforces type safety, auditability, and observability from the moment it starts.

The core problem MXCP solves is the “first‑principles” gap that many AI tool builders face: data quality, security, and operational confidence are often added after the fact. MXCP addresses this by insisting on a data‑model‑first workflow, where every tool’s inputs and outputs are typed against vetted dbt models. This guarantees that an AI assistant never receives malformed data, and it allows the server to validate requests before they hit any downstream logic.

Key capabilities of MXCP include:

• Integrated security – OAuth, RBAC, and fine‑grained policy enforcement are declarative in the same config that defines the tool.
• Audit trail and compliance – Every call is logged with user identity, request payload, and outcome, making it trivial to satisfy regulatory audits.
• Type safety across languages – SQL for data access and Python for logic coexist under a unified schema, with automatic validation at runtime.
• Testing harness – Built‑in commands for unit tests, integration tests, linting of metadata, and LLM‑centric evaluation ensure that the tool behaves as expected before it ever reaches production.
• Observability – OpenTelemetry support gives distributed tracing and metrics out of the box, while drift detection monitors schema changes across environments.

In practice, MXCP shines in scenarios where AI assistants must interact with mission‑critical data. A financial institution can expose a “get_customer_360” tool that pulls enriched customer profiles from dbt marts, while MXCP guarantees that only authorized users can access sensitive fields and that every request is logged for compliance. A logistics company can combine SQL queries on inventory tables with Python logic that calculates optimal routing, all wrapped in a single MCP server that auto‑scales and reports performance metrics.

By embedding security, validation, testing, and observability into the development lifecycle, MXCP lets teams ship AI‑enabled services that meet enterprise standards without the usual engineering overhead. Developers who already use MCP can adopt MXCP by simply generating a configuration file and running ; the resulting server is immediately ready for integration with Claude, OpenAI’s agents, or any other MCP‑compatible client.