AutoMCP

workflow

InsAutoMCP: Automating MCP Server Generation with AI

The Inspiration 💡 Creating MCP servers manually is tedious—writing boilerplate code, defining tool schemas, handling authentication. We asked: what if we could automate this?

AutoMCP bridges API documentation and AI agent integration. Developers upload OpenAPI/Swagger docs and get a production-ready MCP server in minutes, not hours.

What We Learned 📚 Prompt Engineering: Structured, context-rich prompts with clear examples produce better code. We learned to break complex tasks into focused prompts (types → tools → server).

Schema Resolution: OpenAPI's $ref references create complex dependency graphs. We implemented recursive resolution with cycle detection to handle nested schemas.

Error Resilience: External APIs fail. We added retry logic, model validation, and auto-detection to gracefully handle Gemini API issues.

Type Safety: Generating TypeScript that compiles requires careful schema-to-type conversion and handling optional vs required parameters.

How We Built It 🏗️ Architecture Upload → Parser → Transformer → Gemini AI → Generator → ZIP Package

Parsing Layer (src/parsers/)

OpenAPI 3.x parser (JSON/YAML) Extracts endpoints, parameters, schemas, auth schemes Recursive $ref resolution with cycle detection

Transformation Layer (src/transformers/)

Normalizes data for AI consumption Reduces token usage while preserving context Creates endpoint summaries

Gemini Integration (src/gemini/)

Multi-stage code generation: Types → Tool Definitions → Implementations → Server Retry logic with exponential backoff Model validation and auto-detection Extracts code from markdown responses

Package Generator (src/generators/)

Assembles complete npm package Generates package.json, tsconfig.json, config files Creates ZIP archive for distribution

REST API (src/api/)

Express.js endpoints: upload, generate, download, status Local file storage with UUID sessions File upload handling with multer Tech Stack TypeScript + Express.js Google Gemini AI for code generation js-yaml for OpenAPI parsing @modelcontextprotocol/sdk for MCP protocol Challenges We Faced 🚧

Model Availability: gemini-3.0-pro didn't exist, gemini-1.5-pro wasn't available. Solution: Implemented model validation with fallback to gemini-pro.
URL Construction: Generated code used new URL() which failed with relative paths. Solution: Switched to string concatenation with trailing slash handling.
Schema References: Complex $ref graphs with potential cycles. Solution: Recursive resolution with visited set for cycle detection.
Token Limits: Large APIs exceeded limits. Solution: Multi-stage generation (types separately, tools individually).
Type Safety: Ensuring generated TypeScript compiles. Solution: Explicit schema-to-type conversion with proper annotations.

Impact 🌟 Time Savings: Hours → minutes for MCP server creation Zero Boilerplate: Eliminates repetitive code writing Protocol Compliance: Ensures MCP standards automatically Production-Ready: Generated code includes error handling, types, docs Future Enhancements 🚀 Postman collection support Web UI dashboard Batch generation Cloud storage integration CI/CD templates Technical Highlights 🔬 Performance: Parallel tool generation, schema caching, lazy loading

Security: Path validation, file size limits, UUID sessions, input sanitization

Code Quality: TypeScript strict mode, comprehensive error handling, detailed logging piration