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Visualize as an offline interactive HTML explorer

visualize_data

Generate an interactive HTML explorer from a local data file. Optionally filter, aggregate, or group data first; users get sortable tables, column stats, and charts in one offline file.

Instructions

Turn a local data file (optionally after a query) into ONE self-contained, fully-offline, interactive HTML explorer file on disk (sortable/filterable table + column stats + charts). Returns the path. The user can open it in any browser; data never leaves the machine and the file has zero external requests. Use this to hand the user a shareable, explorable artifact instead of a plain text table.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesPath to the data file.
limitNo
titleNoTitle shown in the explorer.
whereNoRow filters (ANDed together).
offsetNo
selectNoColumns to keep in the output.
group_byNoGroup rows by these columns.
order_byNo
out_pathNoWhere to write the .html (default: a temp file).
aggregateNoAggregations to compute per group (with group_by).
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only say readOnlyHint is false; the description goes further and discloses the write behavior (file on disk), the return value (path), and important behavioral constraints (fully offline, zero external requests, data never leaves the machine). This is exactly the kind of context an agent needs beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core transformation and remains compact, but there is mild redundancy between 'fully-offline,' 'zero external requests,' and 'data never leaves the machine.' All sentences earn their place, though one could be trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 10 parameters, nested objects, and no output schema, the description covers the high-level purpose and output but omits operational details such as supported input formats, default title/limit behavior, and whether an existing out_path is overwritten. It is adequate for selection but not fully complete for invocation edge cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 70%, so the schema already explains most parameters such as path, where, select, group_by, out_path, and aggregate. The description adds only high-level framing ('optionally after a query') and does not document the undocumented params (limit, title, offset, order_by), but the missing semantics are largely inferable from names and schema positions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Turn ... into') with a concrete resource and result: a local data file becomes a self-contained interactive HTML explorer. It clearly distinguishes this from siblings by emphasizing a shareable, explorable offline artifact rather than a plain text table.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear context: use when the user wants a shareable, explorable artifact instead of a plain text table, implying this over preview/query output. It does not explicitly name alternatives or exclusion conditions relative to list_data_files, describe_data, or diff_data, so it falls short of explicit when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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