Dot Object Transformer MCP for AI. Map complex data structures for any destination.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

The Dot Object Transformer handles complex data structures by mapping nested JSON objects to simple dot-notation keys for spreadsheets, or reconstructing those flat records back into deeply nested API payloads.

What your AI can do

Transform dot object

Flattens deeply nested JSON objects into dot-notation keys, or reconstructs a nested object from flat dictionary inputs.

Flatten Nested JSON

Transforms deep, multi-level JSON objects into a single set of flat keys using dot notation.

Rebuild Complex Payloads

Converts simple dictionaries with dot-notation keys back into fully nested and structured JSON objects for API calls.

Preserve Data Integrity

Maintains complex structures, including arrays and null values, during the transformation process.

Ask an AI about this

Included with Plan

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AI Agent

Dot Object Transformer: 1 Tool

This single tool allows you to convert data between deeply structured JSON and simple dot-notation key maps, perfect for serialization and deserialization tasks.

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 Dot Object Transformer on Vinkius

Transform Dot Object

Flattens deeply nested JSON objects into dot-notation keys, or reconstructs a nested object from flat dictionary inputs.

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 Dot Object Transformer 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": {
  "dot-object-transformer": {
    "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 Dot Object Transformer 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:

"dot-object-transformer": {
  "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
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with Dot Object Transformer, 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
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by dot-object. 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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No stored credentials

DLP Enforced

Policy on every call

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Token Compression

~60% cost reduction

Your data is protected. See how we built it.

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.

Data formats rarely match up.

Today, if your agent pulls data from one system—say, an API that nests addresses under a 'contact' object—and you need to put it into a spreadsheet for analysis, the process is messy. You spend time writing boilerplate code or manually mapping JSON keys (`user.address.zip`) to CSV columns. If the API changes its nesting by even one level, your entire workflow breaks.

With this MCP, that structural headache vanishes. Whether you're going from a complex nested object to a flat list of attributes for a spreadsheet, or rebuilding the structure for an outbound call, `transform_dot_object` handles the mapping reliably. You get clean data structures every time.

Get reliable JSON payloads with transform_dot_object.

Before this MCP, building a payload for an API often meant manually traversing and rebuilding nested objects in code. This was tedious, error-prone, and required knowing the exact pathing of every single field involved. It's a constant source of bugs.

Now, you simply tell your agent what needs to be converted, and `transform_dot_object` handles the deep structural work. You get robust, predictable payloads that reliably hit the target API endpoint.

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What your AI can actually do with this

When you pull data from an external source via an agent, the structure is almost always deep and hierarchical. But if your goal is a CSV export or populating a web form, that nesting gets in the way. That's where this MCP comes in. It figures out how to take complex JSON—like user profiles with nested addresses and arrays of items—and flatten it deterministically into readable columns using dot-notation keys (e.g., user.address.zip).

Conversely, if you get a flat list of form fields but need to send that data to an API that expects a fully structured payload, this tool reconstructs the object perfectly. It's essential for building reliable pipelines; connecting it through Vinkius gives your agent access to industry-standard tools right alongside everything else.

Built · Hosted · Managed by Vinkius Dot Object Transformer - Flatten & Rebuild JSON Data

Server ID 019e388d-9d1d-72bf-be42-54db319c648b

Vinkius Inspector

Compliance Grade A+

Score 100/100

Report View Report ↗

Here's how it actually works

The bottom line is that this tool makes sure your data structure matches whatever system you're sending it to, without losing any information along the way.

You provide your agent with the input data—this could be a deeply nested JSON object or a flat dictionary of key-value pairs.

Your agent invokes transform_dot_object, specifying whether it needs to flatten the structure (for export) or reconstruct it (for API submission).

The MCP returns the output: either a clean, single-level data map perfect for CSVs, or a perfectly structured JSON payload ready for consumption.

What Changes When You Connect

Eliminates manual JSON mapping. You stop writing custom, brittle code every time a schema changes and just call transform_dot_object.

Guaranteed structural fidelity. It handles arrays and null values perfectly, so you don't lose data when moving between nested and flat formats.

Reliable API preparation. When your agent needs to send structured data, this tool reliably rebuilds the payload from simple inputs.

Seamless reporting prep. Exporting complex user records for a spreadsheet is now trivial; transform_dot_object flattens everything you need into columns.

Saves development time. Instead of writing bespoke serialization logic, you use this single point of truth for object manipulation.

See it in action

01 01

Exporting User Data to a Spreadsheet

A data analyst pulls user records from an internal API. The JSON is nested (e.g., user.address.city). They need this data in Excel. Instead of writing complex mapping code, your agent uses transform_dot_object to flatten the structure, giving clean columns like 'user.address.city' that can be loaded directly into CSV.

02 02

Handling Multi-Form Submissions

A user fills out a complex form with separate fields for billing and shipping addresses. The backend needs to send this data as one nested JSON object. By using transform_dot_object, the agent takes the flat input data and reliably reconstructs it into the required deep payload structure.

03 03

Migrating Legacy APIs

You're moving an old service that outputs simple, key-value pairs to a modern API that requires full nesting. You use transform_dot_object to take the flat keys (like user.id) and reliably convert them into the deep JSON object structure the new API demands.

04 04

Pre-processing Chatbot Inputs

A user provides a block of structured data within a chat conversation, but your downstream system requires it to be flat for database logging. You use transform_dot_object on the input JSON payload to generate simple dot-notation keys before passing it off.

The honest tradeoffs

Manual Key Pathing

Anti-pattern

Writing code that manually extracts data using data['user']['address']['city']. This breaks immediately if the API changes a single key name or nesting level.

The Fix

Use transform_dot_object to handle all structural mapping. It abstracts away the underlying pathing, so your workflow stays stable even when schemas shift.

Assuming Flat Data

Anti-pattern

Treating a payload that contains arrays or nested objects as simple key-value pairs, leading to data loss or silent failure.

The Fix

transform_dot_object preserves all complex types. Whether you're flattening or rebuilding, it keeps the integrity of arrays and nulls.

Using Generic Dictionary Tools

Anti-pattern

Relying on basic dictionary tools that only handle one level deep and ignore path complexity.

The Fix

This MCP is built specifically for deep, recursive object manipulation. It handles nesting at any depth, making it far more powerful than simple key-value mappers.

When It Fits, When It Doesn't

Use this MCP if your primary challenge is structural data migration—that means moving between deeply nested JSON structures and flat CSV/form formats. If you are always converting the shape of the object, use transform_dot_object. Do NOT use it if: 1) You only need to filter or read a single value (e.g., 'What is the user ID?'). Use standard data retrieval tools for that. 2) Your input and output structures are already guaranteed to be identical in shape. Only use this MCP when you must change the structure's depth or dimensionality.

Questions you might have

How does transform_dot_object handle nested arrays? +

It preserves array integrity during both flattening and unflattening. If an object contains a list of items, it keeps that list structure intact while mapping the keys.

Is the Dot Object Transformer bidirectional? +

Yes, it's fully bidirectional. You can flatten nested JSON into dot-notation keys, and then convert those flat keys back into a complex, nested object.

When I run transform_dot_object, what happens if my input data contains null values or empty arrays? +

It handles nulls and empty arrays without issue. The tool maintains the structure of these missing fields, so your output JSON payload remains accurate for downstream systems.

What should I do if transform_dot_object encounters malformed or incomplete dictionary data? +

If you provide a corrupted input, the MCP will return an explicit error message detailing which part of the structure failed. This allows your AI client to catch the problem and adjust the payload before proceeding.

Does using transform_dot_object require any local dependencies or setup outside of Vinkius? +

No, you don't need to install anything locally. Since this MCP is managed by Vinkius, your AI client simply connects and uses the tool directly through our standard protocol.

How scalable is transform_dot_object when dealing with extremely large JSON payloads? +

It processes data efficiently, designed to handle complex structures up to typical payload limits. If you hit a size wall, the tool will report the limit and suggest chunking your data.

Is the data passed through transform_dot_object kept private when using it with my agent? +

Yes, all data processing happens within the secure connection framework of Vinkius. We treat your input JSON and resulting transformed data as confidential.

Can I use transform_dot_object if my source data isn't strictly JSON format? +

No, this MCP is specifically built to map standard JSON objects. You must feed it valid JSON; the tool cannot process formats like XML or YAML directly.

Does it preserve arrays? +

Yes, arrays are flattened with numeric indices (e.g. "items.0.name") and restored perfectly on unflatten.

Connect to your AI in seconds.

Transform dot object

Dot Object Transformer: 1 Tool

Make your AI actually useful.

Transform Dot Object

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

Data formats rarely match up.

Get reliable JSON payloads with transform_dot_object.

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

Exporting User Data to a Spreadsheet

Handling Multi-Form Submissions

Migrating Legacy APIs

Pre-processing Chatbot Inputs

The honest tradeoffs

Manual Key Pathing

Assuming Flat Data

Using Generic Dictionary Tools

When It Fits, When It Doesn't

Questions you might have