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read_chart

Extract axes, series, trends, and key values from charts, graphs, plots, tables, and dashboards. Marks estimates when exact pixels are unclear.

Instructions

Read charts, graphs, plots, tables, and dashboards. Extracts axes, series, trends, and key values (marks estimates when exact pixels are unclear).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageNoLocal file path, file:// URI, http(s) URL, data URL, or base64 image data
imagesNoOne or more images. Prefer this for multi-image chats: ["path/a.png", "path/b.png"] or [{source, label: "1"}, {source, label: "2"}]. Labels default to "1", "2", ...
promptNoOptional extra instruction for the vision model
mimeTypeNoOptional MIME type hint for a single bare-base64 `image` input, e.g. image/png
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses a key behavioral trait: 'marks estimates when exact pixels are unclear', which adds transparency about output reliability. It also implies a read-only, non-destructive operation, though it could further explain output format or limitations.

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

Conciseness5/5

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

The description is two short sentences, front-loaded with the tool's core purpose and immediately followed by extraction details. Every clause provides value—no filler or repetition of the tool name.

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

Completeness4/5

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

Without an output schema, the description helps by listing what is extracted (axes, series, trends, key values) and the estimate behavior. The parameter schema covers multi-image usage, so the description doesn't need to repeat that. It's sufficient for a read-only chart tool, though it could be more explicit about return structure.

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 description coverage is 100%, so the baseline is 3. The description does not add any parameter-specific details beyond what the schema already documents. It doesn't explain the difference between 'image' and 'images' or how the 'prompt' parameter modifies behavior, but the schema itself is thorough.

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 ('Read') and resource ('charts, graphs, plots, tables, and dashboards'), and enumerates what it extracts ('axes, series, trends, and key values'). This clearly distinguishes it from sibling tools like ocr_image or describe_image, which handle different tasks.

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?

The description provides clear context for when to use the tool (any chart-like visual input) but does not explicitly mention alternatives or when not to use it. The context is specific enough for an agent to decide, but it stops short of naming sibling tools or exclusions.

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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