Skip to main content

Introduction to MintMCP

MintMCP is an enterprise gateway for Model Context Protocol (MCP) that provides authentication, authorization, and governance for AI-to-data connections.

What is MCP?​

Model Context Protocol (MCP) is an open standard that enables AI assistants to securely connect to data sources and tools. Major companies including Block, Bloomberg, and Shopify have adopted MCP for their AI infrastructure. As AI systems evolve from simple chat interfaces to tool-using agents, MCP provides the standardized interface for these connections.

Enterprise Requirements​

Deploying MCP in enterprise environments presents several technical challenges:

  • Authentication & Authorization: Open-source MCP servers typically lack enterprise authentication mechanisms
  • Access Control: No built-in role-based access control (RBAC) or attribute-based access control (ABAC)
  • Audit & Compliance: Missing request logging and audit trails required for regulatory compliance
  • Multi-Client Support: Different teams use various AI clients (ChatGPT, Claude, Cursor) with incompatible protocols
  • Observability: No telemetry, monitoring, or usage analytics
  • Deployment Complexity: Difficult to manage multiple MCP servers across an organization

MintMCP Architecture​

MintMCP addresses these requirements with a gateway architecture that sits between AI clients and MCP servers:

Security Layer​

  • OAuth 2.0 and SAML authentication
  • Tool-level permissions with RBAC/ABAC policies
  • Request/response logging for audit compliance
  • Secure credential management for downstream services

Protocol Translation​

  • Converts between different AI client protocols (OpenAPI, MCP, REST)
  • Maintains compatibility with all major AI platforms
  • Handles protocol version differences

Management Features​

  • Centralized tool registry and discovery
  • Usage analytics and rate limiting
  • Request routing and load balancing
  • Health monitoring and alerting

Integration Patterns​

MintMCP supports common enterprise integration patterns:

  • Data Warehouses: Connect Snowflake, BigQuery, Databricks for natural language queries
  • Communication Platforms: Integrate Slack, Teams, email for context-aware AI responses
  • Document Systems: Access Google Drive, SharePoint, Confluence for knowledge retrieval
  • Custom Tools: Deploy proprietary tools with standardized interfaces

Technical Implementation​

MintMCP is built with:

  • Core gateway written in Go for performance
  • PostgreSQL for configuration and audit storage
  • Redis for caching and rate limiting
  • OpenTelemetry for observability
  • Kubernetes-native deployment

Deployment Options​

MintMCP offers two deployment models:

  • Cloud: Managed service with SLA guarantees
  • Self-Hosted: Deploy on your infrastructure with full control

Getting Started​

To deploy MintMCP in your environment, see Get Access for requirements and setup instructions.