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kehvinbehvin/json-mcp-filter

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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.

JSON Server MCP server

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

  • πŸ”„ 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
  • πŸ“¦ Auto Chunking - Handles large datasets with automatic 400KB chunking
  • πŸ›‘οΈ 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

πŸ› οΈ 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

πŸ“‹ 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}}})
// β†’ "Recommended chunks: 6"
// 2. Get chunks
json_filter({filePath: "./large.json", shape: {"users": {"id": true}}})
// β†’ Chunk 0 + metadata
json_filter({filePath: "./large.json", shape: {"users": {"id": true}}, chunkIndex: 1})
// β†’ Chunk 1 + metadata

πŸ”’ Security Notice

Remote Data Fetching: This tool fetches data from HTTP/HTTPS URLs. Users are responsible for:

βœ… Safe Practices:

  • Verify URLs point to legitimate endpoints
  • Use trusted, public APIs only
  • Respect API rate limits and terms of service
  • Review data sources before processing

❌ Maintainers Not Responsible For:

  • External URL content
  • Privacy implications of remote requests
  • Third-party API abuse or violations

πŸ’‘ Recommendation: Only use trusted, public data sources.

πŸš€ Quick Start

Option 1: NPX (Recommended)

# No installation required
npx json-mcp-filter@latest

Option 2: Global Install

npm install -g json-mcp-filter@latest
json-mcp-server

Option 3: From Source

git clone <repository-url>
cd json-mcp-filter
npm install
npm run build

βš™οΈ MCP Integration

Claude Desktop

Add to your configuration file:

{
 "mcpServers": {
 "json-mcp-filter": {
 "command": "npx",
 "args": ["-y", "json-mcp-filter@latest"]
 }
 }
}

Claude Code

# Add via CLI
claude mcp add json-mcp-filter npx -y json-mcp-filter@latest

Or add manually:

  • Name: json-mcp-filter
  • Command: npx
  • Args: ["-y", "json-mcp-filter@latest"]

πŸ”§ Development

Commands

npm run build # Compile TypeScript
npm run start # Run compiled server 
npm run inspect # Debug with MCP inspector
npx tsc --noEmit # Type check only

Testing

npm run inspect # Interactive testing interface

πŸ“ Project Structure

src/
β”œβ”€β”€ index.ts # Main server + tools
β”œβ”€β”€ strategies/ # Data ingestion strategies
β”‚ β”œβ”€β”€ JsonIngestionStrategy.ts # Abstract interface
β”‚ β”œβ”€β”€ LocalFileStrategy.ts # Local file access
β”‚ └── HttpJsonStrategy.ts # HTTP/HTTPS fetching
β”œβ”€β”€ context/
β”‚ └── JsonIngestionContext.ts # Strategy management
└── types/
 └── JsonIngestion.ts # Type definitions

🚨 Error Handling

Comprehensive Coverage

  • Local Files: Not found, permissions, invalid JSON
  • Remote URLs: Network failures, auth errors (401/403), server errors (500+)
  • Content Size: Auto-reject >50MB with clear messages
  • Format Detection: Smart detection of HTML/XML with guidance
  • Rate Limiting: 429 responses with retry instructions
  • Processing: Quicktype errors, shape filtering issues

All errors include actionable debugging information.

⚑ Performance

Processing Times

File Size Processing Time
< 100 KB < 10ms
1-10 MB 100ms - 1s
10-50 MB 1s - 5s
> 50 MB Blocked

Size Protection

  • 50MB maximum for all sources
  • Pre-download checking via Content-Length
  • Memory safety prevents OOM errors
  • Clear error messages with actual vs. limit sizes

Best Practices

  • Use json_dry_run first for large files
  • Filter with json_filter before schema generation
  • Focus shapes on essential fields only

🌐 Supported Sources

  • Public APIs - REST endpoints with JSON responses
  • Static Files - JSON files on web servers
  • Local Dev - http://localhost during development
  • Local Files - File system access

πŸ’‘ Common Workflows

LLM Integration:

  1. API returns large response
  2. json_filter extracts relevant fields
  3. Process clean data without noise
  4. json_schema generates types for safety

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