# TOML Parser Engine MCP

> TOML Parser Engine converts structured configuration data bidirectionally between TOML and JSON. It handles complex formats—like arrays of tables, nested structures, and datetime values—used in Rust's Cargo.toml, Python's pyproject.toml, and Cloudflare's wrangler.toml. Gives your AI client perfect fidelity when moving config data.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Tags:** toml, json, configuration, serialization, data-parsing, devops

## Description

**Your AI client needs to read config files. This engine handles converting structured settings bidirectionally between TOML and JSON.**

When you're working with complex configuration systems—like those found in `Cargo.toml`, `pyproject.toml`, or `wrangler.toml`—you ain't dealing with simple key-value pairs. You're moving deep, nested data structures, arrays of tables, and specific datetime formats. This is where the **`parse_toml`** tool steps in. It lets your agent move configuration data between TOML and JSON while maintaining perfect fidelity, no matter how complicated the format gets.

You can use this engine to convert raw content one way or the other by calling `parse_toml`, telling it exactly if you need 'toml-to-json' or 'json-to-toml'. It handles all complex data types deterministically; that means when you run a conversion, you get consistent results every single time.

If your process starts with raw TOML content—say, you got a block of text from a `Cargo.toml` file—you feed it into the parser and specify 'toml-to-json'. The engine takes that raw TOML structure and converts it into a fully structured JSON object. This resulting JSON is clean, easy for your client to use, and perfectly represents every piece of data in the original TOML file.

Conversely, if you're working with a pre-existing JSON structure—maybe another part of your system generated a configuration as an object—you pass that JSON to `parse_toml` and specify 'json-to-toml'. The engine then writes out valid TOML syntax. It makes sure the output is syntactically correct, so you don't have to manually fix indentation or escaping characters.

This isn't just a basic parser; it fully supports the entire TOML 1.0 specification. This means you get proper handling for nested tables, which are crucial when grouping related settings. It also manages arrays of tables—a common feature in modern configuration files that lets you list multiple instances of the same structure. For those complex scenarios, like inline tables (where key-value pairs live on one line) or multiline strings, it handles them without losing any data integrity.

It even correctly processes specific types like datetime values and other complex data types inherent in modern development tools. When your agent needs to process a config file that uses TOML syntax—whether it's dealing with build dependencies, project metadata, or deployment settings—you know this tool handles the full spectrum of complexity. It turns raw configuration text into an actionable, structured format (JSON) and back again, ensuring zero data loss across the entire conversion round trip.

## Tools

### parse_toml
Converts TOML configuration files to JSON, or JSON back to TOML. You provide the content and specify if you need 'toml-to-json' or 'json-to-toml'. It handles all complex data types deterministically.

## Prompt Examples

**Prompt:** 
```
Convert this Cargo.toml to JSON so I can inspect the dependencies.
```

**Response:** 
```
TOML Conversion: JSON output generated with all nested tables preserved.
```

**Prompt:** 
```
Generate a valid wrangler.toml from this JSON config.
```

**Response:** 
```
TOML Conversion: Valid wrangler.toml generated.
```

**Prompt:** 
```
Parse this pyproject.toml and extract the project metadata as JSON.
```

**Response:** 
```
TOML Conversion: Project metadata extracted with name, version, and dependencies.
```

## Capabilities

### Convert TOML to JSON
Pass raw TOML content, and the engine converts it into a fully structured JSON object.

### Convert JSON to TOML
Pass a JSON structure, and the engine writes out valid TOML configuration syntax.

## Use Cases

### Migrating a Python project's metadata
A developer updates their `pyproject.toml` and needs to check how the resulting structure looks for a JSON API endpoint. They send the TOML content to an agent, which uses `parse_toml(..., 'toml-to-json')`. The output gives them clean JSON metadata (name, version, dependencies) they can use directly in their service code.

### Updating Cloudflare worker settings
A DevOps engineer receives a new configuration schema as pure JSON. They need to generate the required `wrangler.toml` file for deployment. They run `parse_toml(json_data, 'json-to-toml')`. The agent returns a perfectly formatted and valid TOML file ready to commit.

### Inspecting Rust dependencies
You are debugging dependency conflicts in a project's `Cargo.toml`. Instead of manually reading the complex nested structure, you feed the file into an agent calling `parse_toml(cargo_toml, 'toml-to-json')`. The clean JSON output makes inspecting all dependencies straightforward.

### Testing format compatibility
You write a core configuration block in TOML. You want to test if another service that only accepts JSON can read it correctly. Use `parse_toml` first to convert the TOML to JSON, and then pass the resulting JSON string into your testing environment.

## Benefits

- Stop losing data when converting configs. The `parse_toml` tool handles nested tables, arrays of tables, and complex types, ensuring zero fidelity loss whether you're going TOML to JSON or vice versa.
- Manage multiple project stacks in one place. You can reliably convert between formats used by Rust (`Cargo.toml`), Python (`pyproject.toml`), and Cloudflare (`wrangler.toml`) without switching tools.
- Handle complex data types automatically. The engine properly processes datetime values and multiline strings, which are often the trickiest parts of configuration file conversions.
- Keep your agent focused on code. Instead of writing custom parsing logic for every config type, just call `parse_toml` to handle serialization tasks instantly.
- Build reliable pipelines. By converting structured data reliably, you can build composable agents that treat configuration files as first-class, predictable data objects.

## How It Works

The bottom line is that you don't have to worry about syntax errors when moving config data between formats.

1. Feed the agent the raw content (TOML or JSON) and specify the conversion direction ('toml-to-json' or 'json-to-toml').
2. The engine processes the input, interpreting complex structures like nested tables and arrays of tables to ensure data integrity.
3. Receive the output: a perfectly formatted string in the target format (JSON or TOML).

## Frequently Asked Questions

**How do I convert my Cargo.toml to JSON using the TOML Parser Engine?**
Pass the raw `Cargo.toml` content and specify 'toml-to-json' in the `parse_toml` tool call. The engine will generate a perfectly structured JSON output, preserving all dependencies and metadata.

**Does the TOML Parser Engine handle complex data types?**
Yes. It fully supports advanced TOML 1.0 features, including nested tables, arrays of tables, inline tables, datetime values, and multiline strings. You won't lose fidelity.

**What is the best way to use `parse_toml` for my pyproject.toml?**
To extract metadata, pass your `pyproject.toml` content into the tool and request conversion to JSON (`toml-to-json`). This makes project details like name, version, and dependencies easy for your agent to process.

**Can I convert any TOML file using this MCP Server?**
The `parse_toml` tool handles standard TOML 1.0 syntax used in common developer files like those from Rust, Python, and Cloudflare. It's designed for these structured config formats.

**Does `parse_toml` guarantee zero data loss during round-trip conversion?**
Yes, it handles full round-trip fidelity. You can convert TOML to JSON and back again; the engine preserves nested tables, arrays of tables, and all original content exactly.

**Does the TOML Parser Engine support the latest TOML 1.0 specification?**
Absolutely. The server supports the full TOML 1.0 spec, including advanced types like datetime values and multiline strings alongside standard tables.

**When using `parse_toml`, how do I direct it to convert JSON back into TOML?**
You must pass the explicit direction parameter, specifying 'json-to-toml'. This tells the engine exactly which conversion path you need for your data.

**Can the TOML Parser Engine correctly handle complex structures like arrays of tables?**
Yes. It processes complex data structures reliably, ensuring that both arrays of tables and inline tables retain their structure regardless of the conversion direction.

**Does it support TOML 1.0 spec?**
Yes. @iarna/toml fully supports the TOML 1.0 specification including all edge cases like nested tables, inline tables, and datetime values.

**Can I convert JSON back to TOML?**
Yes. Use direction "json-to-toml" to serialize a JSON object back into valid TOML format with proper sections and formatting.

**What files does this commonly work with?**
Cargo.toml (Rust), pyproject.toml (Python), wrangler.toml (Cloudflare Workers), Hugo config.toml, and any TOML-based configuration file.