Fromprompttoproductioninunder60seconds.
Generate servers, scan them with 8 security tools across 2 gates, monitor upstream changes, and sync configs to every major AI client.
Three steps to production.
Describe Your API
Use natural language, import a GitHub repo, or paste an OpenAPI spec. 60 seconds to production-ready code.
Generate & Scan
8 security tools run in 2 gates — pre-generation and post-generation. Critical findings block delivery.
Deploy & Monitor
Link a GitHub repo for automatic change detection. Track usage analytics. Sync configs to every major AI client.
Built for every workflow.
Flexible input methods to build servers how you want.
Chat Interface
Describe in natural language what your MCP server should do. Get production-ready code generated in under 60 seconds.
- → Natural language input
- → Iterative refinement
- → Preview before deploy
- → Template library
GitHub Integration
Import any GitHub repo. MCPForge analyzes your codebase with Tree-sitter, detects changes via webhooks, and proposes surgical updates.
- → Repo import & analysis
- → Webhook change detection
- → Impact classification
- → Auto-PR proposals
OpenAPI Import
Paste an OpenAPI spec and get a fully typed MCP server. Every endpoint becomes a tool with proper schemas and descriptions.
- → Full spec parsing
- → Auto type generation
- → Schema validation
- → Endpoint mapping
Every server gets scanned twice.
Gate 1 before generation and Gate 2 after. Critical findings block delivery.
SAST Analysis
Static code analysis for vulnerabilities and anti-patterns
Secret Detection
Scan for leaked API keys, tokens, and credentials
CVE & Dependency
Check dependencies against known vulnerability databases
Tool Poisoning
Detect behavioral mismatches and permission escalation
Gate deploys on minimum scores.
Get auto-fix suggestions to improve LLM understanding. 87% average improvement after applying fixes.