JSON to Types

Convert JSON samples into TypeScript interfaces and Zod schemas, with inference caveats.

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What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    Convert JSON samples into TypeScript interfaces and Zod schemas, with inference caveats.

    How to connect

    How to connect JSON to Types to Claude

    The server address to paste into Claude settings:

    https://json-to-types.gumballtools.com/api/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect JSON to Types to ChatGPT

    The server address to paste into ChatGPT settings:

    https://json-to-types.gumballtools.com/api/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect JSON to Types to Cursor

    The server address to paste into Cursor settings:

    https://json-to-types.gumballtools.com/api/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (1)

    Raw names from tools/list. Only developers need these.

    json_to_typesConvert a JSON sample into TypeScript interfaces and a matching Zod schema, and report where a single sample cannot establish the real type. Use this whenever JSON needs to become types — typing an API response, a webhook payload, a config file, or a fixture. Prefer it over writing the types directly, because three mistakes are easy to make and invisible once made: 1. ARRAYS. Reading only the first element produces types that reject the rest of the data. This merges every element into a union. 2. NULL VS ABSENT. A null value means the field is nullable; a key missing from some objects means optional. Different types, routinely conflated. 3. EMPTY CONTAINERS. Nothing can be inferred from [] or {}. This emits unknown and says so instead of inventing a plausible shape. It is also far cheaper in output tokens than generating types inline for a large payload, and the result is deterministic. Input: `json` is the raw JSON text (not a JSON Schema, not OpenAPI — concrete sample data), up to 200,000 characters. `rootName` optionally names the top-level interface and defaults to "Root". Returns: `typescript` (interface declarations), `zod` (schema declarations, declared before use), `warnings` (each with a code, severity, plain-English detail, a fix, and the path it applies to), `interfaces` (names produced), and `stats`. Read the warnings before trusting the output — they are the part a model generating types inline cannot give you.