Convert JSON, XML or a Jupyter notebook into Markdown with inspectable tables and protected code.
Drag and drop your files, or choose them from your device.
.json · .xml · .ipynb
JSON, XML and notebook files are read in your browser. Notebook code is displayed without being executed.
Upload JSON, XML or a Jupyter Notebook in IPYNB format, or paste JSON and XML text into the workspace. Choose an export containing only the fields you need, especially when a larger dataset includes personal or commercially sensitive records.
Check the live Markdown editor, GFM tables and LLM token count for every section produced. Inspect missing properties, nested values and notebook outputs so a compact representation remains understandable without silently turning unrelated records into one flattened table.
Save the Markdown or copy a carefully selected table and its explanatory notes. Data parsing happens locally, and notebook code is never run; clear any optional recent output in this tab when your review is complete.
Arrays of similarly shaped JSON records make practical tables with a consistent set of columns. Missing properties leave empty cells rather than shifting another value into place, allowing readers to compare records without treating an absent field as a fabricated answer.
Nested data needs a visible representation of its context. Objects, sibling collections and metadata are retained in sections or readable serialized content where one rectangular table would hide their relationships, giving you a chance to simplify the result deliberately rather than lose detail invisibly.
XML attributes and element text can each carry important information. Mixed content is read in order and repeated values remain visible; declarations that would request external document resources are rejected instead of being used to retrieve additional content during conversion.
Notebook Markdown cells, code cells and available captured outputs are represented without running a kernel. Raw cells remain identifiable, and unsupported rich output is described so a missing chart rendering is not confused with an empty experiment or a successful new execution.
A structured export becomes easier to discuss when its field names and collection boundaries are visible to a human reader. Before conversion, decide whether a timestamp is a measurement date, an update time or another kind of identifier, then explain that meaning near the generated table. The converter can display the available values but cannot infer a schema’s business rules or tell you which records should count toward an analysis.
For an LLM context pack, a small table with a field glossary is often more useful than a large unannotated payload. Use the token counter to compare those choices and remove secrets before copying the result to another service. Notebook conversions are particularly useful for reviewing an existing experiment: code and stored results travel together, but their appearance does not prove the notebook was run recently, in order, or against the current input data.
A property absent from a JSON record appears as an empty place in the corresponding table column, rather than shifting later fields or guessing a replacement. Explicit nulls and empty strings may have different meanings in your source system, so add a note if readers must distinguish them. Check the generated column names against the records before using the table as evidence of completeness.
Not always. XML can contain attributes, repeated elements and text interleaved with child elements, so forcing every document into a single table could discard its structure. The converter uses tables for suitable repeated records and keeps other content in readable sections. Review parent-child relationships and repeated values, then choose a simpler reporting layout yourself if the original hierarchy is too complex for a useful comparison.
No notebook code is executed. The IPYNB file is read as a saved document containing Markdown, source code and any outputs already stored inside it. Text outputs can appear beside their cells, while richer outputs receive a description when they cannot be reproduced as Markdown. You should run the notebook separately in a trusted environment if your task requires fresh results or confirmation that its cells still work.
A nested object or array can represent a relationship that would disappear if its values were simply joined into an unlabeled string. Keeping a readable serialized form or a dedicated section makes that relationship explicit and preserves sibling content for inspection. Decide which level matters to your report, then rewrite its labels in the editor; conversion should expose the data, not choose an undocumented interpretation on your behalf.