bitflight-devops/mcp-json-yaml-toml

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A structured data reader and writer like 'jq' and 'yq' for AI Agents

概览

A token-efficient, schema-aware MCP server for safely reading and modifying JSON, YAML, and TOML files Getting Started • CLI Usage • Available Tools • Development Stop AI coding tools from breaking your data files. No more grep guesswork, hallucinated fields, or non-schema-compliant data added to files. This MCP server gives AI assistants a strict, round-trip safe interface for working with structured data. AI coding tools often destroy structured data files: - They through huge json, yaml, and toml files (like json logs, or AI transcript files) and guess at keys. - They fields that never existed. - They use that leave files in invalid states. - They break and . - They can't changes before writing. provides AI assistants with proper tools for structured data: - : Extract exactly what you need without loading entire files. - : Enforce correctness using SchemaStore.org or custom schemas. - : ; preserve comments and formatting. - : JSON, YAML, and TOML through a unified interface.

README

以下为原文 README 节选，完整内容请查看原文。

mcp-json-yaml-toml

A token-efficient, schema-aware MCP server for safely reading and modifying JSON, YAML, and TOML files

Getting Started • CLI Usage • Available Tools • Development

Stop AI coding tools from breaking your data files. No more grep guesswork, hallucinated fields, or non-schema-compliant data added to files. This MCP server gives AI assistants a strict, round-trip safe interface for working with structured data.

The Problem

AI coding tools often destroy structured data files:

They grep through huge json, yaml, and toml files (like json logs, or AI transcript files) and guess at keys.
They hallucinate fields that never existed.
They use sed and regex that leave files in invalid states.
They break YAML indentation and TOML syntax.
They can’t validate changes before writing.

The Solution

mcp-json-yaml-toml provides AI assistants with proper tools for structured data:

Token-efficient: Extract exactly what you need without loading entire files.
Schema validation: Enforce correctness using SchemaStore.org or custom schemas.
Safe modifications: Enforced validation on write; preserve comments and formatting.
Multi-format: JSON, YAML, and TOML through a unified interface.
Directive-based detection: Support for # yaml-language-server, #:schema, and $schema keys in all formats.
Constraint-based guided generation: Native LMQL support for proactive validation of partial inputs.
Local-First: All processing happens locally. No data ever leaves your machine.
Transparent JIT Assets: The server auto-downloads yq if missing and fetches missing schemas from SchemaStore.org for local caching.

[!NOTE]

JSONC Support: Files with .jsonc extension (JSON with Comments) are fully supported for reading, querying, and schema validation. However, write operations will strip comments due to library limitations.

Getting Started

Prerequisites

Python ≥ 3.11 installed.

在 GitHub 查看完整 README

安装

uvx mcp-json-yaml-toml

新手？先看 mcpskill.org 教程 →

配置

{
  "mcpServers": {
    "json-yaml-toml": {
      "command": "uvx",
      "args": ["mcp-json-yaml-toml"]
    }
  }
}