json

This MCP server is a Rust-based JSON MCP server that exposes a suite of JSON file operations optimized for large datasets and efficient interaction with LLMs. It provides tools for reading JSON with optional JSONPath filtering, writing or updating JSON with multiple merge strategies, performing complex JSONPath queries, validating JSON syntax, and interactive help. The server follows the Model Context Protocol (MCP) and communicates via JSON-RPC over stdin/stdout, enabling LLMs and clients to invoke discrete actions such as json-read, json-write, json-query, json-validate, and json-help. Typical usage involves starting the server as a background or foreground process and issuing JSON-RPC requests to call the available tools. Each tool includes parameters for file paths, JSONPath expressions, formats, and merge modes to tailor behavior for large files and streaming scenarios. This setup is especially useful when integrating JSON operations into LLM workflows where precise, streaming-friendly processing and clear error messages are important for promptable reasoning and robust tooling.

To use the tools, you would send a JSON-RPC request with the method corresponding to the tool name (e.g., json-read, json-write, json-query) and include an arguments object detailing the required inputs. For example, to read a file with a JSONPath filter, you would call json-read with arguments including file_path and json_path, optionally setting limit and output_format. For queries, json-query accepts a file_path, a json_path or query expression, and an output format. For writing, json-write supports different modes such as replace or merge variants, with optional backup handling. The json-help tool provides in-depth guidance and examples to help you construct valid requests and understand JSONPath syntax. The server emphasizes memory efficiency through streaming and supports large files without loading entire datasets into memory.

Prerequisites:

• A supported environment with Rust toolchain if you plan to build from source (optional).
• cURL or a network access to download installation scripts or prebuilt binaries.

Quick installation (recommended):

• Install the server globally using Cargo (Rust). This compiles and installs the json-mcp-server binary on your system.

# Install Rust toolchain if not already installed (Ubuntu/Debian example)
sudo apt-get update
sudo apt-get install -y build-essential curl

# Install via Cargo (Rust) - this fetches the prebuilt binary and sets it up
cargo install json-mcp-server

# Run the server to verify installation
json-mcp-server --version

Alternative installation script (Linux/macOS):

# Linux/macOS
curl -fsSL https://raw.githubusercontent.com/ciresnave/json-mcp-server/main/scripts/install.sh | bash

# Windows PowerShell
iwr https://raw.githubusercontent.com/ciresnave/json-mcp-server/main/scripts/install.ps1 | iex

Other distribution methods include downloading pre-built binaries from the GitHub Releases page and installing via your system package manager if available (deb, rpm, or AUR pages are linked in the README). Refer to the README for exact commands per platform.

Verification:

json-mcp-server --version
json-mcp-server --help

Launching the server (examples):

# Run in foreground (default)
json-mcp-server

# Or with a release build (if installed via cargo with --release)
cargo run --release

Tips and common issues:

• Ensure your environment has sufficient permissions for reading/writing JSON files, especially when handling large datasets.
• If you encounter JSONPath syntax errors, use json-help to retrieve prompts and examples for correct expressions.
• For very large files, prefer streaming options and pagination to avoid high memory usage; set appropriate limit and offset values where supported.
• Use descriptive file_path and consider enabling backup options in write operations to prevent data loss on failed writes.
• The MCP server communicates over JSON-RPC; include proper id fields in your requests to track responses, and use the json-help topic to discover additional capabilities.
• When integrating with LLMs, provide concise, deterministic outputs (e.g., specific formats like json or pretty) to simplify prompt processing and result extraction.