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Introducing MintMCP - Enterprise Gateway for Model Context Protocol

· 3 min read
Lutra AI Team
Lutra AI Team
Building the future of AI infrastructure

We're launching MintMCP, an enterprise gateway that provides authentication, authorization, and governance for Model Context Protocol (MCP) connections between AI clients and data sources.

Background​

Model Context Protocol (MCP) has emerged as the standard for connecting AI assistants to tools and data sources. Companies like Block, Bloomberg, and Shopify have adopted MCP for their AI infrastructure. However, deploying MCP in enterprise environments presents significant technical challenges around security, governance, and multi-client support.

MintMCP addresses these challenges with a gateway architecture that provides enterprise-grade features while maintaining compatibility with the MCP ecosystem.

Technical Architecture​

Core Components​

MintMCP implements a gateway pattern between AI clients and MCP servers:

AI Clients (ChatGPT, Claude, Cursor) 
    ↓
MintMCP Gateway
    ↓
MCP Servers (Snowflake, Slack, Custom Tools)

Key Features​

Authentication & Authorization

  • OAuth 2.0 and SAML support
  • Tool-level permissions with RBAC/ABAC
  • API key management
  • Session handling across different AI clients

Protocol Translation

  • Converts between OpenAPI (ChatGPT), native MCP (Claude), and REST formats
  • Maintains backward compatibility with protocol versions
  • Handles authentication token exchange

Audit & Compliance

  • Complete request/response logging
  • User activity tracking
  • Compliance-ready audit trails
  • Data governance policy enforcement

Operations

  • Centralized tool registry
  • Usage analytics and monitoring
  • Rate limiting and quota management
  • Health checks and alerting

Implementation Examples​

Data Warehouse Access​

tools:
  - name: snowflake_query
    type: mcp
    endpoint: snowflake-mcp:8080
    permissions:
      - group: analysts
        databases: [analytics_db]
        operations: [SELECT]
        row_limit: 10000

Multi-Source Queries​

# Query across Slack and documentation
User: "What was decided about the API redesign?"
MintMCP: 
  1. Search Slack #engineering channels
  2. Query Confluence for API docs
  3. Return aggregated results

Self-Service Analytics​

Enable natural language queries without SQL knowledge:

  • "Show customer acquisition cost by channel"
  • "Calculate month-over-month revenue growth"
  • "Find top customers by lifetime value"

Technical Specifications​

Deployment Requirements​

Cloud Deployment

  • Managed service with 99.9% SLA
  • Multi-region availability
  • Automatic scaling

Self-Hosted Deployment

  • Kubernetes 1.24+
  • PostgreSQL 14+ for configuration
  • Redis 6+ for caching
  • 4 vCPU, 8GB RAM minimum per instance

Integration Support​

  • ChatGPT Enterprise (via Custom Actions)
  • Claude for Work (native MCP)
  • Cursor and AI coding tools
  • Custom agents via REST API

Roadmap​

Current Release

  • Multi-MCP federation
  • Basic authentication and permissions
  • Snowflake, BigQuery, Databricks connectors
  • Audit logging

Q3 2025

  • Advanced RBAC with attribute policies
  • Semantic layer for business metrics
  • Additional connectors (Slack, Drive, Confluence)

Q4 2025

  • Query optimization and caching
  • Workflow orchestration
  • Custom tool SDK

Getting Started​

MintMCP is available for enterprise deployments. To evaluate MintMCP:

  1. Contact enterprise@mintmcp.com
  2. Schedule technical discovery session
  3. Deploy pilot with your use cases
  4. Scale to production

About Lutra AI​

MintMCP is built by Lutra AI. We have extensive experience building MCP servers, clients, and AI agent infrastructure. Our team includes engineers who have deployed AI tools at scale in enterprise environments.

For technical questions or to request access, contact us at enterprise@mintmcp.com.