MCP-to-API Gateway

Transform any command, script, or API into a standardized Model Context Protocol server that LLMs can use seamlessly. Complete gateway solution with embedded analytics and enterprise security.

Get Started View Architecture

Three Execution Modes

Local commands, containerized execution, and HTTP webhooks/APIs

Local Execution

Direct command execution with native performance

Container Isolation

Docker-based execution with complete isolation

Webhook/API Gateway

HTTP client for upstream API integration

Architecture Flow

Modular design with comprehensive monitoring

MCP Client (Claude, Custom) → HTTP/STDIO → MCPFier → Local/Container/Webhook → Analytics
MCP Client
Claude Desktop
→
MCPFier
Gateway
→
Execution
3 Modes
→
Analytics
SQLite DB

Transport Layer

  • STDIO for desktop clients
  • HTTP for enterprise deployments
  • MCP 2025-06-18 compliant

Authentication

  • API key-based auth
  • Granular permissions
  • Request validation

Execution Engines

  • Local command execution
  • Docker container isolation
  • HTTP webhook client

Security Features

  • Container isolation
  • Resource limits
  • Timeout enforcement

Embedded Analytics

Comprehensive monitoring with web dashboard

MCPFier Analytics Dashboard

Total HTTP Requests
1,247
Success Rate
98.5%
Auth Success Rate
99.2%
Avg HTTP Request Time
12ms
Avg MCP Tool Time
185ms

Command Metrics

  • Execution statistics by mode
  • Success/failure rates
  • Performance timing
  • Most used commands

HTTP Server Metrics

  • Request/response tracking
  • Authentication metrics
  • Endpoint usage patterns
  • Error categorization

Upstream API Metrics

  • Webhook call success rates
  • API latency tracking
  • Error breakdown by type
  • Upstream service health

Simple Configuration

YAML-based setup with auto-discovery

config.yaml 
commands:
  # Local execution
  - name: list-files
    script: ls
    args: ["-la"]
    description: "List directory contents"
    
  # Container execution
  - name: python-analysis
    script: python
    args: ["/app/analyze.py"]
    container: "python:3.9-slim"
    
  # Webhook/API execution
  - name: weather-api
    description: "Get current weather"
    webhook:
      url: "https://api.weather.com/v1/current"
      method: "GET"
      auth:
        type: "bearer"
        token: "${WEATHER_API_KEY}"

server:
  http:
    enabled: true
    host: "localhost"
    port: 8080
    auth:
      enabled: true
      api_keys:
        "mcpfier_key_123":
          permissions: ["*"]

analytics:
  enabled: true
  database_path: "~/.mcpfier/analytics.db"

Use Cases

Enterprise Integration

Transform internal APIs and workflows into MCP tools

Development Tools

Linting, testing, building, deployment automation

Infrastructure

Health checks, log analysis, backup operations

API Gateway

Bridge MCP clients to existing REST APIs and webhooks

Hybrid Workflows

Combine local scripts, containerized tools, and external APIs

AI Agents

Enable LLMs to access enterprise systems and external services