MCP JSON Server

A Model Context Protocol (MCP) server for JSON file operations, schema inference, and data transformation.

Overview

This MCP server provides comprehensive JSON manipulation tools and resources that can be used with Claude Desktop and other MCP-compatible clients. It offers powerful capabilities for reading, writing, transforming, and analyzing JSON data from files and URLs.

Features

🔧 Tools

Core JSON Tools:

get_data - Fetch JSON data from files or URLs with optional JSON pointer access
get_schema - Infer JSON schema from data sources
schema_of - Generate JSON schema from text input
write_json - Write JSON data to files with formatting options
update_json - Update specific values using JSON pointers
delete_json_key - Remove keys or array elements
merge_json - Merge JSON data with multiple strategies (update/replace/deep)
transform_json - Transform data (flatten/unflatten/keys_only/values_only)

🚀 Market Data Optimization Tools (Token Efficient):

get_ohlcv_summary - Get OHLCV statistical summaries instead of raw data
get_market_stats - Calculate field statistics (min/max/mean) for numeric data
query_market_data - Filter and paginate large datasets efficiently
get_top_performers - Get top N records by any metric
get_data_sample - Get representative samples of large datasets

📁 Resources

json://file/{path} - Direct access to JSON files
json://url/{encoded_url} - Direct access to JSON from URLs

🎯 Key Capabilities

JSON Pointer support (RFC 6901) for precise data access
Schema inference with JSON Schema Draft 2020-12 compliance
Multiple merge strategies for combining JSON data
Data transformation utilities (flatten/unflatten nested objects)
Support for both local files and HTTP/HTTPS URLs
Comprehensive error handling and validation

Installation

Option 1: Install from PyPI (when published)

pip install mcp-json-server

Option 2: Install from Source

git clone <repository-url>
cd mcp-json-server
pip install -e .

Option 3: Development Installation

git clone <repository-url>
cd mcp-json-server
pip install -e ".[dev]"

Quick Start

1. Start the Server

# Using the console script
mcp-json-server

# Or run directly
python src/mcp_json_server.py

2. Configure Claude Desktop

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "mcp-json-server": {
      "command": "mcp-json-server"
    }
  }
}

3. Basic Usage Examples

Read JSON Data

# Read entire file
get_data(source="data.json")

# Read with JSON pointer
get_data(source="config.json", pointer="/database/host")

# Read from URL  
get_data(source="https://api.example.com/data.json")

Generate Schemas

# Infer schema from file
get_schema(source="users.json", title="User Schema")

# Generate schema from JSON text
schema_of(json_text='{"name": "Alice", "age": 30}')

Update Data

# Update a specific value
update_json("config.json", "/database/port", 5432)

# Add new data
update_json("settings.json", "/features/new_feature", true)

Transform Data

# Flatten nested objects
transform_json("nested.json", "flat.json", "flatten")

# Extract just the keys structure
transform_json("data.json", "keys.json", "keys_only")

JSON Pointer Usage

This server supports JSON Pointer (RFC 6901) for precise data access:

{
  "users": [
    {"name": "Alice", "profile": {"city": "NYC"}},
    {"name": "Bob", "profile": {"city": "LA"}}
  ],
  "meta": {"total": 2}
}

Pointer Examples:

"" or "/" → entire document
"/users" → users array
"/users/0" → first user
"/users/0/name" → "Alice"
"/users/1/profile/city" → "LA"
"/meta/total" → 2

Alternative Formats:

"/users/0/name" (standard)
"#/users/0/name" (with hash prefix)
"users/0/name" (without leading slash)

Tool Reference

Data Access Tools

`get_data(source, pointer="")`

Fetch JSON data with optional JSON pointer filtering.

source: File path or URL
pointer: JSON pointer for data selection (optional)

`get_schema(source, pointer="", title="Schema")`

Generate JSON schema from data source.

source: File path or URL
pointer: JSON pointer to specific data (optional)
title: Schema title

`schema_of(json_text, title="Schema")`

Generate JSON schema from JSON text input.

json_text: JSON string to analyze
title: Schema title

Data Modification Tools

`write_json(file_path, data, indent=2)`

Write JSON data to file with formatting.

file_path: Output file path
data: JSON data to write
indent: Indentation spaces

`update_json(file_path, json_pointer, new_value)`

Update specific value using JSON pointer.

file_path: Target JSON file
json_pointer: Path to value
new_value: New value to set

`delete_json_key(file_path, json_pointer)`

Delete key or array element.

file_path: Target JSON file
json_pointer: Path to delete

`merge_json(target_file, source_data, strategy="update")`

Merge JSON data with different strategies.

target_file: File to merge into
source_data: Data to merge
strategy: "update", "replace", or "deep"

Data Transformation Tools

`transform_json(source_path, output_path, transformation)`

Transform JSON data structure.

source_path: Input file
output_path: Output file
transformation: "flatten", "unflatten", "keys_only", "values_only"

Resources

File Resource: `json://file/{path}`

Direct access to local JSON files.

Example:

json://file/config.json
json://file/data/users.json

URL Resource: `json://url/{encoded_url}`

Direct access to JSON from HTTP/HTTPS URLs.

Example:

json://url/https%3A//api.example.com/data.json

Note: URLs must be percent-encoded

Development

Running Tests

# Run all tests
python test_runner.py

# Run unit tests only  
python -m pytest tests/

# Run specific test file
python tests/test_mcp_json.py

Project Structure

mcp-json-server/
├── src/
│   └── mcp_json_server.py    # Main server implementation
├── tests/
│   └── test_mcp_json.py      # Unit tests
├── test_runner.py            # Comprehensive test runner
├── usage_example.py          # Usage examples
├── pyproject.toml           # Project configuration
└── README.md               # This file

Dependencies

mcp>=1.0.0 - Model Context Protocol framework
requests>=2.28.0 - HTTP client for URL fetching

Development Dependencies:

pytest>=7.0.0 - Testing framework
pytest-asyncio>=0.21.0 - Async test support
black>=22.0.0 - Code formatting
ruff>=0.1.0 - Linting and code quality

Common Use Cases

Configuration Management

# Read current config
config = get_data(source="app-config.json")

# Update database port
update_json("app-config.json", "/database/port", 5433)

# Add feature flag
update_json("app-config.json", "/features/new_api", true)

API Data Processing

# Fetch from API
api_data = get_data(source="https://api.service.com/users")

# Save locally
write_json("users-backup.json", api_data)

# Transform for analysis
transform_json("users-backup.json", "users-flat.json", "flatten")

Schema Generation & Validation

# Generate schema from sample data
schema = get_schema(source="sample-data.json", title="Data Schema")

# Save schema
write_json("data-schema.json", schema)

# Generate schema for specific subset
user_schema = get_schema(source="users.json", pointer="/users/0", title="User")

Error Handling

The server provides detailed error messages for common issues:

Invalid JSON syntax
File not found errors
Network connectivity issues
Invalid JSON pointer paths
Type mismatch errors

Contributing

Fork the repository
Create a feature branch
Make your changes
Run tests: python test_runner.py
Submit a pull request

License

MIT License - see LICENSE file for details.

Support

Issues: Report bugs and request features via GitHub issues
Documentation: See usage_example.py for more examples
Testing: Run python test_runner.py to verify functionality

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Enables comprehensive JSON file operations including reading, writing, transforming, and analyzing JSON data from local files and URLs. Supports JSON pointer access, schema inference, data merging, and specialized market data optimization tools.

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