TOML Parser Engine MCP for AI. Move config data between TOML and JSON flawlessly.

Q: 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 parsetoml tool call. The engine will generate a perfectly structured JSON output, preserving all dependencies and metadata.

Q: What is the best way to use parsetoml 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.

Q: Can I convert any TOML file using this MCP Server?

The parsetoml 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.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

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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.

What your AI can do

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.

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.

Ask an AI about this

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AI Agent

TOML Parser Engine: 1 Tool for Data Conversion

Use the parse_toml tool to convert configuration files—like Cargo.toml or pyproject.toml—between TOML and JSON formats with guaranteed fidelity.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using TOML Parser Engine on Vinkius

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...

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The TOML Parser Engine integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "toml-parser-engine": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the TOML Parser Engine tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"toml-parser-engine": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
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Publish to catalog or keep private

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Make Your AI Do More

Start with TOML Parser Engine, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,100+ others, all in one place
Add new capabilities to your AI anytime you want
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by @iarna/toml. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Config conversion shouldn't require manual schema mapping.

Right now, if you need to move data between configuration files—say, from a Python-generated JSON object into the TOML format required by your CI/CD pipeline—you spend time validating syntax. You copy sections, paste them elsewhere, and constantly check for missing brackets or misplaced array indicators. It’s tedious and prone to human error.

With this MCP server, you just pass the raw data and tell the agent what conversion you need. The `parse_toml` tool handles every bracket, every nested table, and every unique data type automatically. You get clean, ready-to-use code output instantly.

TOML Parser Engine: Convert configuration files in seconds.

The manual steps that disappear are the validation passes and the cross-reference checks. You never have to worry about whether your agent correctly interpreted an array of tables or a datetime value across two different file formats—the engine manages it all.

Now, you treat configuration files like any other data source: reliable, predictable, and instantly convertible.

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What your AI can actually do with this

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.

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Who is this actually for?

Any engineer who spends time managing infrastructure configuration files across multiple languages. Think DevOps engineers running CI/CD pipelines, or backend developers writing cross-platform configs for Rust and Python services. If your workflow involves moving structured data between TOML and JSON, you need this.

DevOps Engineer

Uses the tool to convert wrangler.toml or other cloud config files from a high-level schema (JSON) into deployable format (TOML).

Backend Developer

Swaps dependencies between a project's JSON requirements file and its native TOML configuration (pyproject.toml) for version control.

Software Architect

Tests cross-language compatibility by ensuring that data defined in one format (e.g., Rust's Cargo.toml) can be perfectly mapped and read back into another (JSON).

What Changes When You Connect

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.

See it in action

01 01

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.

02 02

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.

03 03

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.

04 04

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.

The honest tradeoffs

Copy/pasting config data

Anti-pattern

A user copies a complex Cargo.toml section and pastes it into a simple text editor or tries to manually convert it in the chat window, losing proper array formatting or nested table structure.

The Fix

Always use the parse_toml tool. Send the entire file content to the agent and ask for conversion (e.g., 'Convert this TOML to JSON'). This preserves all data structures.

Assuming simple key-value pairs

Anti-pattern

Treating a configuration block as simple text, ignoring that it contains an array of tables or nested type definitions, leading to invalid syntax.

The Fix

The parse_toml tool handles the entire TOML 1.0 spec. It knows how to interpret complex structures like arrays and nested blocks, giving you accurate output.

Ignoring data direction

Anti-pattern

Sending JSON content but failing to specify 'json-to-toml' when calling the tool, leading the agent to reject the input or perform an unintended conversion.

The Fix

Always explicitly pass the desired direction ('toml-to-json' or 'json-to-toml') as a parameter when using parse_toml.

When It Fits, When It Doesn't

Use this engine if your primary task is moving structured data between TOML and JSON. Specifically, if you are working with project manifest files (like Cargo.toml, pyproject.toml) or cloud configuration templates (wrangler.toml). It's mandatory for any agent that needs to guarantee zero-loss conversion across these specific formats.

Don't use this if: 1) You just need simple text parsing (e.g., extracting a single email address from a paragraph). Use a general regex tool instead. 2) The data structure is fundamentally different and not based on the TOML/JSON standard (e.g., proprietary XML formats). If it's not one of those two, this engine won't help.

Questions you might have

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.

Connect to your AI in seconds.

Parse toml

TOML Parser Engine: 1 Tool for Data Conversion

Make your AI actually useful.

Parse Toml

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

Config conversion shouldn't require manual schema mapping.

TOML Parser Engine: Convert configuration files in seconds.

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Migrating a Python project's metadata

Updating Cloudflare worker settings

Inspecting Rust dependencies

Testing format compatibility

The honest tradeoffs

Copy/pasting config data

Assuming simple key-value pairs

Ignoring data direction

When It Fits, When It Doesn't

Questions you might have