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mcp-server-generator

MCP Tool

Ismail-2001/mcp-server-generator

An enterprise-grade compiler for the Agentic Era: Translates complex OpenAPI specs into production-ready MCP servers with built-in Semantic Compression and Resource Grouping for the 2M token economy.

Install

$ npx loaditout add Ismail-2001/mcp-server-generator

Platform-specific configuration:

.claude/settings.json
{
  "mcpServers": {
    "mcp-server-generator": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-server-generator"
      ]
    }
  }
}

Add the config above to .claude/settings.json under the mcpServers key.

About

Legacy REST for Humans. Optimized MCP for Intelligence.

> "One command. Any API. Instant MCP server."

[](https://opensource.org/licenses/MIT) [](https://www.typescriptlang.org/) [](https://modelcontextprotocol.io/) [](https://github.com/Ismail-2001/mcp-server-generator)

The mcp-server-generator is a high-performance, enterprise-grade compiler that transforms any OpenAPI/Swagger specification into a production-ready Model Context Protocol (MCP) server.

LLMs often struggle with large OpenAPI specs due to context window limits and "hallucination noise." This tool solves that by applying Semantic Compression and Resource Grouping to deliver ultra-efficient tool definitions that "just work."

---

🏗️ System Architecture

Our 5-stage pipeline is designed for maximum resilience, correctness, and LLM efficiency.

graph TD
    A[OpenAPI/Swagger Spec] -->|Stage 1| B(Parser & Normalizer)
    B -->|Stage 2| C(Semantic Analyzer)
    C -->|Stage 3| D(Resource Mapper)
    D -->|Stage 4| E(Description Optimizer)
    E -->|Stage 5| F(Code Emitter)
    
    subgraph "The Compiler Pipeline"
    B
    C
    D
    E
    F
    end
    
    F --> G[Production MCP Server]
    G --> H[LLM / Claude Desktop]

---

🔥 Key Innovations
🧠 Semantic Compression (Optimizer)

Stop wasting thousands of tokens on "This endpoint returns a list of...". Our optimizer strips implementation details and boilerplate, enforcing a strict token budget per tool.

  • Boilerplate Removal: Regex-based stripping of common API phrases.
  • Contextual Truncation: Sentence-level pruning that preserves core intent.
  • Token Budgeting: Ensures every tool fits comfortably in a 200k-2M

Tags

ai-agentscompilercontext-optimizationmcp-servermodel-context-protocolopenaiproduction-readyrest-apiserver-generatorswaggertypescript-llm

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Quality Signals

0
Installs
Last updated25 days ago
Security: AREADME

Safety

Risk Levelmedium
Data Access
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Details

Sourcegithub-crawl
Last commit3/25/2026
View on GitHub→

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