title	description	sidebar_position	slug
Features Overview	Complete overview of MXCP's enterprise features for production data-to-AI workflows	1	/features

MXCP Features Overview

MXCP provides a comprehensive set of enterprise features designed for production data-to-AI workflows. Unlike simple data connectors, MXCP offers security, governance, quality assurance, and operational excellence.

🔒 Security & Governance

Authentication & Authorization

OAuth 2.0 Integration: GitHub, Atlassian, Salesforce, and custom providers
Session Management: Secure token handling with persistence
Role-Based Access Control: Fine-grained permissions and scopes
API Key Support: For programmatic access
Stateless Mode: For serverless deployments

Policy Enforcement

Input Policies: Control who can execute endpoints
Output Policies: Filter sensitive data dynamically
CEL Expressions: Flexible condition evaluation
User Context: Rich context for policy decisions
Field-Level Security: Mask or remove specific fields

Audit Logging

Complete Trail: Every query, result, and error logged
User Attribution: Track who did what and when
Flexible Storage: JSONL files or DuckDB
Query Interface: Search and analyze audit logs
Compliance Ready: Export for regulatory requirements

✅ Quality Assurance

Validation

Schema Validation: Ensure endpoints meet specifications
Type Checking: Validate parameter and return types
SQL Verification: Check query syntax
Reference Validation: Verify file and resource references

Testing

Unit Tests: Test endpoints with various inputs
Assertion Types: Exact match, partial match, exclusions
Policy Testing: Verify access controls work correctly
CI/CD Integration: JSON output for automation

Linting

Metadata Quality: Improve LLM understanding
Best Practices: Suggest descriptions, examples, tags
Severity Levels: Warnings and suggestions
Bulk Analysis: Check entire codebase at once

LLM Evaluation

AI Behavior Testing: Verify LLMs use tools correctly
Safety Checks: Ensure destructive operations are avoided
Context Testing: Validate permission-based access
Model Support: Test with multiple AI models

🔄 Data & Operations

Drift Detection

Schema Monitoring: Track changes across environments
Baseline Snapshots: Compare against known good state
Change Detection: Identify added, modified, removed endpoints
CI/CD Integration: Prevent breaking changes

dbt Integration

Native Support: Run dbt models directly
Local Caching: Use dbt to populate DuckDB
Model Discovery: Automatic model detection
Transformation Pipeline: ETL/ELT workflows

Monitoring & Operations

Health Checks: Endpoint availability monitoring
Performance Metrics: Query execution times
Error Tracking: Detailed error logs and traces
Operational Commands: Direct endpoint execution

🚀 Developer Experience

Type System

Rich Types: Primitives, objects, arrays, dates
Validation: Automatic input/output validation
Constraints: Min/max, patterns, enums
LLM Hints: Help AI understand data types

SQL Reference

DuckDB Syntax: PostgreSQL-compatible analytical SQL
Built-in Functions: User authentication functions
Named Parameters: Safe parameter binding
Extensions: httpfs, json, parquet, and more

Python Reference

Runtime APIs: Database, config, secrets access
Lifecycle Hooks: Server initialization/shutdown/reload
Thread Safety: Concurrent execution support
Type Compatibility: Seamless SQL/Python integration

Plugin System

Python Extensions: Custom functions and UDFs
Provider Plugins: OAuth and authentication
Shared Libraries: Reusable components
Hot Reloading: Development productivity

CLI Tools

Project Management: Init, serve, list
Quality Tools: Validate, test, lint, evals
Operations: Log queries, drift checks
Development: Live reload, debug mode

🔌 Integrations

LLM Platforms

Claude Desktop: Native MCP support
OpenAI Tools: Via adapters
Custom Clients: MCP protocol implementation
Multi-Model: Support various AI providers

Data Sources

DuckDB: Built-in analytical database
SQL Databases: Via DuckDB extensions
APIs: HTTP/REST endpoints
Files: CSV, Parquet, JSON

Secret Management

HashiCorp Vault: Enterprise secret storage
Environment Variables: Simple secret injection
Encrypted Storage: Secure local secrets
Runtime Injection: No secrets in code

📊 Use Cases

MXCP's features enable powerful use cases:

Secure AI Analytics: Give LLMs data access with governance
Compliant Automation: Track all AI actions for audit
Multi-Tenant SaaS: Isolate customer data with policies
Data Products: Package data as AI-ready interfaces
DevOps Automation: Monitor and control infrastructure

Getting Started

Quickstart Guide - Get running in 60 seconds
Configuration - Set up your project
Write Endpoints - Create your first tool
Add Security - Implement access control
Test & Deploy - Ensure quality

MXCP combines enterprise features with developer productivity, making it the ideal platform for production data-to-AI workflows.

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