The Model Context Protocol (MCP) is an open standard by Anthropic that lets AI assistants like Claude, ChatGPT, and Copilot connect to external tools and data sources. An MCP server exposes tools (functions the AI can call), resources (data the AI can read), and prompts (templates for common tasks). MCPForge generates, secures, and maintains these servers — turning any API, database, or service into an AI-accessible tool in under 60 seconds.
Use natural language, import a GitHub repo, or paste an OpenAPI spec. 60 seconds to production-ready code.
8 security tools run in 2 gates — pre-generation and post-generation. Critical findings block delivery.
Link a GitHub repo for automatic change detection. Track usage analytics. Sync configs to every major AI client.
Describe in natural language what your MCP server should do. Get production-ready code generated in under 60 seconds.
Import any GitHub repo. MCPForge analyzes your codebase with Tree-sitter, detects changes via webhooks, and proposes surgical updates.
Paste an OpenAPI spec and get a fully typed MCP server. Every endpoint becomes a tool with proper schemas and descriptions.
Every server gets scanned twice — Gate 1 before generation (SAST, secrets, CVEs, API surface) and Gate 2 after (tool poisoning, behavioral analysis, permission escalation). Critical findings block delivery.
Static code analysis for vulnerabilities and anti-patterns
Scan for leaked API keys, tokens, and credentials
Check dependencies against known vulnerability databases
Detect behavioral mismatches and permission escalation
Score any MCP server across 6 dimensions. Get auto-fix suggestions to improve LLM understanding. Gate deploys on minimum scores. 87% average improvement after applying fixes.
Drop-in proxy intercepts MCP calls and reports per-tool analytics: calls, latency P50/P95/P99, errors, and client distribution. 12.8K calls tracked per day on average. Set alerts and export via API, webhooks, or OpenTelemetry.
Start free with Gate 1 security. Upgrade for the full pipeline.