一个强大的模型上下文协议（MCP）服务器，为本地文件和远程 HTTP/HTTPS 端点提供 JSON 模式生成和过滤工具。采用 Quicktype 构建，实现稳健的 TypeScript 类型生成。

完美用途：过滤大型JSON文件和API响应，只提取与LLM上下文相关的数据，同时保持类型安全。

✨ 主要特色

• 🔄 模式生成 - 使用 quicktype 将 JSON 转换为 TypeScript 接口
• 🎯 智能过滤 - 通过基于形状的过滤提取特定字段
• 🌐 远程支持 - 支持 HTTP/HTTPS URL 和 API 端点
• 📦 自动分块 - 自动400KB分块处理大型数据集
• 🛡️ 大小保护 - 内置50MB限制，带内存安全
• ⚡ MCP Ready - 与 Claude Desktop 和 Claude Code 无缝集成
• 🚨 智能错误 - 清晰且可操作的错误信息及调试信息

🛠️ 可用工具

json_schema

从 JSON 数据生成 TypeScript 接口。

参数：

• filePath：本地文件路径或HTTP/HTTPS地址

示例：

json_filter

利用基于形状的过滤和自动分块提取特定字段，适用于大型数据集。

JSON MCP Filter

A powerful Model Context Protocol (MCP) server that provides JSON schema generation and filtering tools for local files and remote HTTP/HTTPS endpoints. Built with quicktype for robust TypeScript type generation.

Perfect for: Filtering large JSON files and API responses to extract only relevant data for LLM context, while maintaining type safety.

✨ Key Features

• 🔄 Schema Generation - Convert JSON to TypeScript interfaces using quicktype
• 🎯 Smart Filtering - Extract specific fields with shape-based filtering
• 🌐 Remote Support - Works with HTTP/HTTPS URLs and API endpoints
• 📦 Auto Chunking - Handles large datasets with automatic 400KB chunking
• 🛡️ Size Protection - Built-in 50MB limit with memory safety
• ⚡ MCP Ready - Seamless integration with Claude Desktop and Claude Code
• 🚨 Smart Errors - Clear, actionable error messages with debugging info

🛠️ Available Tools

json_schema

Generates TypeScript interfaces from JSON data.

Parameters:

• filePath: Local file path or HTTP/HTTPS URL

Example:

// Input JSON
{"name": "John", "age": 30, "city": "New York"}

// Generated TypeScript
export interface GeneratedType {
    name: string;
    age:  number;
    city: string;
}

json_filter

Extracts specific fields using shape-based filtering with automatic chunking for large datasets.

Parameters:

• filePath: Local file path or HTTP/HTTPS URL
• shape: Object defining which fields to extract
• chunkIndex (optional): Chunk index for large datasets (0-based)

Auto-Chunking:

• ≤400KB: Returns all data

• 400KB: Auto-chunks with metadata

json_dry_run

Analyzes data size and provides chunking recommendations before filtering.

Parameters:

• filePath: Local file path or HTTP/HTTPS URL
• shape: Object defining what to analyze

Returns: Size breakdown and chunk recommendations

📋 Usage Examples

Basic Filtering

// Simple field extraction
json_filter({
  filePath: "https://api.example.com/users",
  shape: {"name": true, "email": true}
})

Shape Patterns

// Single field
{"name": true}

// Nested objects
{"user": {"name": true, "email": true}}

// Arrays (applies to each item)
{"users": {"name": true, "age": true}}

// Complex nested
{
  "results": {
    "profile": {"name": true, "location": {"city": true}}
  }
}

Large Dataset Workflow

// 1. Check size first
json_dry_run({filePath: "./large.json", shape: {"users": {"id": true}}})
// → "Recommended chunks: 6"

// 2. Get chunks