# JSON viewer and editor. Keep your data local.

JSON documents · Windows and macOS

Inspect JSON records in a table, read complete nested values and save reviewed changes to a separate JSON file. RowCove processes your documents locally with a native Rust engine—without a dataset upload.

[Download for Windows ↓](https://rowcove.com/download/windows) [Download for macOS ↓](https://rowcove.com/download/macos)

Free Preview: up to 100 read-only records. No account or card. Version 0.1.1 · Windows x64 · macOS Apple Silicon · [Install guide →](https://rowcove.com/download)

**How can I view and edit a JSON file offline?** Open the file in RowCove, check the detected JSON reader and inspect the free read-only first page. With Pro, filter or sort the document, switch to Edit, stage supported cell changes and use Save copy. Ordinary JSON documents use a bounded reader: up to 256 MiB and two million records. For larger record streams, use [JSONL or NDJSON](https://rowcove.com/jsonl-viewer) .

**On this page** [Open a local JSON file](https://rowcove.com/json-viewer#open) [Read nested records](https://rowcove.com/json-viewer#nested) [Filter and sort](https://rowcove.com/json-viewer#query) [Edit and save a copy](https://rowcove.com/json-viewer#edit) [JSON vs JSONL](https://rowcove.com/json-viewer#choose) [Privacy and local AI](https://rowcove.com/json-viewer#privacy)

## Open JSON without pasting it into an online tool

Dataset exports, experiment metadata and configuration records can contain information you want to keep on your own device. Choose Open data, select a .json file in the native file picker, review its sample and open the document. You do not need Python, a notebook or a cloud import to inspect supported records.

Free Preview needs no account or card. It shows a read-only first page of up to 100 records, with complete cell inspection. Pro adds the supported filtering, sorting and edit-copy workflow. The [same Pro plan](https://rowcove.com/pricing) covers supported CSV, JSONL, Parquet and Arrow operations; there is no separate per-format purchase.

RowCove presents JSON as records, rather than a code editor with syntax highlighting. An array supplies multiple records; a single object or scalar supplies one record. This workflow is useful when your question concerns fields and examples, rather than writing arbitrary JSON source code.

## Inspect nested fields without losing the record

Nested object fields appear as table columns such as meta.split. Select a cell to read its complete value in the record inspector. Arrays and other structured values need their full context: a short visible excerpt is not the complete training conversation, label set or metadata object.

The following small example is synthetic mock data only.

Nested JSON example · synthetic (mock) data

| record_id | meta.split | label | Review |
| --- | --- | --- | --- |
| mock_001 | demo | unreviewed | Inspect the original record |
| mock_002 | demo | reviewed | Compare a saved copy |

Scroll horizontally to inspect all fields. Keyboard: focus the table and use the arrow keys.

Check identifiers, numeric precision, nulls and missing fields before relying on a comparison. Choose a numeric sort only when the field really represents numbers; identifiers and category codes may need text ordering.

## Answer a document-wide question, then inspect the matching records

Find loaded rows searches the page already in view. To ask a question about the opened document, use the supported Pro filter and sort controls. Choose the relevant field, set the predicate and inspect the returned scope. Keep the source row with your findings.

For example, filter a mock category or split, inspect missing labels, then order a numeric score before reviewing candidate records. Keep the filter, ordering and document version with your notes so the result can be reproduced. A matching first page alone does not establish anything about a different file or a collection of shards.

JSON documents are parsed within a deliberate size budget. This differs from a line-oriented JSONL stream or projected columnar access. The published [20 GB mock CSV comparison](https://rowcove.com/compare) measures CSV; its timings do not establish JSON throughput.

## Change a field and save a separate JSON copy

1. Open the document and check the intended source record and field.

2. Switch to Edit and select the cell.

3. Review the complete value, enter the replacement and choose Stage edit.

4. Review the staged change; undo it if needed.

5. Choose Save copy and a new destination in the native save dialog.

6. Reopen the output and verify the changed field, other records and expected types.

Supported nested-field edits are saved back into JSON structure, rather than exporting the flattened table as the document. The source stays unchanged. Saved JSON may use different indentation or formatting, so use it as a data copy rather than a byte-for-byte source-code preservation workflow.

JSON types matter. A valid JSON literal can represent a number, boolean, null, array, object or quoted string. To keep a string that looks like another JSON type—for example the text “true”—enter it as a quoted JSON string and verify the output. Use [the getting-started guide](https://rowcove.com/docs) for cell inspection, staged changes and safe-copy workflows.

## Choose JSON, JSONL or a columnar workflow for the actual file

Choose the matching local reader

| Source | Useful workflow | What to know |
| --- | --- | --- |
| JSON document | Inspect a bounded array or object; edit supported fields | 256 MiB document limit; up to two million records |
| JSONL / NDJSON | Browse line-oriented records and supported training-data shards | One JSON record per line; not an arbitrary JSON document |
| Parquet | Select columns and supported partitions | Reader-specific query and bounded editing limits |
| Arrow / Feather | Inspect typed columnar records locally | Supported IPC variant and source capabilities matter |

A document larger than the JSON budget is not made streamable by renaming it .jsonl. Produce a valid record-oriented export using the source system, or choose a suitable columnar export. For pretraining, SFT and preference data, follow the [nested training-record guide](https://rowcove.com/guides/jsonl-training-data) and [post-training data workflow](https://rowcove.com/guides/post-training-data) .

## Local processing, with deliberate sharing through MCP

Viewing, querying and saving supported documents run on your device. Account activation, billing and updates use network services separately. When an AI client calls [RowCove MCP](https://rowcove.com/mcp) , requested results are shared with that client and may reach its model provider.

Authorize the intended folder and ask for selected fields and bounded records. Review the tool schema and result scope before expanding the task. Start with the [MCP setup tutorial](https://rowcove.com/docs-mcp) and [complete format capability matrix](https://rowcove.com/formats) .

## Try the first page on your own data.

Start on Windows or macOS: source information and up to 100 records, read-only. Review reader capabilities before planning a full-file Pro workflow.

[Download for Windows ↓](https://rowcove.com/download/windows) [Download for macOS ↓](https://rowcove.com/download/macos)

Version 0.1.1 · Windows x64 · macOS Apple Silicon · [Install guide →](https://rowcove.com/download)

[Reader capabilities →](https://rowcove.com/formats)

## JSON viewing, editing and privacy

Questions, answered

[Contact support →](https://rowcove.com/support)

### Do I need Pro for occasional JSON viewing or formatting?

Start with free Preview if you want to check a supported document and inspect its first page, up to 100 records. For occasional formatting of a small document, a dedicated formatter may already meet your needs. RowCove presents JSON as records and nested fields for a local data workflow.

Consider Pro when you need the supported document-wide filters or sorting, reviewed nested-field edits saved to a separate copy, or authorized MCP access as part of repeated data work. The JSON document reader has a 256 MiB limit and up to two million records. For larger record streams, evaluate the JSONL or columnar workflow against your source before purchasing.

[Choose the right record format](https://rowcove.com/json-viewer#choose) [Decide whether Pro fits your work](https://rowcove.com/pricing#faq-pro-fit)

### Can I edit a JSON document without overwriting the original?

Yes. With Pro, open a supported bounded JSON document, switch to Edit, select the field and review its complete value. Enter a replacement, choose Stage edit and review the change before using Save copy. Choose a new destination, then reopen the output to check the edited value and its type. The original stays unchanged.

Nested-field edits are saved into JSON structure. Valid JSON literals can change a value’s type; quote a replacement when you need a string such as "true" instead of a boolean. Saved formatting can differ from the input. The document reader is limited to 256 MiB and two million records; use a valid JSONL or columnar export for larger record-oriented data.

[Follow the JSON editing steps](https://rowcove.com/json-viewer#edit) [Compare Preview and Pro](https://rowcove.com/pricing)

### What is the difference between viewing JSON and JSONL?

JSON is one document that can contain objects or arrays across many lines. JSONL and NDJSON place a JSON value on each record line, commonly an object. A nested messages array inside a JSONL record is still one source record; the number of messages is not the number of file records.

RowCove detects the reader and displays the operations it supports. The JSON document reader supports bounded queries, nested-field edits and Save copy in Pro, with a 256 MiB document limit and up to two million records. JSONL has a separate streaming and indexed workflow with supported record queries and editing. Dataset-folder scans have their own bounded scope. Inspect representative nesting and types before assuming a training pipeline can consume the result.

[View and edit a JSON document](https://rowcove.com/json-viewer) [Explore JSONL and NDJSON records](https://rowcove.com/jsonl-viewer) [Inspect training records step by step](https://rowcove.com/guides/jsonl-training-data)

### Do my datasets ever have to leave my computer?

You do not need to upload a dataset to RowCove to inspect, query or edit it. Those desktop operations run on your device. Account activation and billing use separate online services for information such as your purchase email, subscription and device access; opening a dataset does not send its contents to those services.

MCP is an explicit sharing path: requested tool results go to the AI client you connect. If that client uses a cloud model, results may reach its provider. Choose the folders, fields and result size you intend to share, and review the client’s own privacy policy before using sensitive data.

[Read the privacy policy](https://rowcove.com/privacy) [Control MCP sharing](https://rowcove.com/docs-mcp#permissions)

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Canonical page: https://rowcove.com/json-viewer

Updated: 2026-11-03

[Related Agent reference](https://rowcove.com/agent/formats.md)