Nativ

extract_image_text

Extract text segments from an image, in reading order (OCR).

Use this as the first step when localizing an image: pull out the
on-image source strings, translate each with `translate` (so TM, style
guides, and brand voice apply), then re-render with `localize_image`.

Args:
    image_path: Public HTTPS URL (hosted) or local path (stdio) to a PNG, JPEG, or WebP image file.

Input Schema

Name	Required	Description	Default
`image_path`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 9, 2026

TDQS

A4.3/5.0

Behavior3/5

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

No annotations are supplied, so the description carries the full behavioral burden. It does disclose the accepted formats (PNG, JPEG, WebP) and the hosting constraint (public HTTPS URL vs local path under stdio), but says nothing about size limits, failure modes, or whether extraction is read-only and side-effect free.

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?

Front-loaded purpose sentence, then the pipeline guidance, then the argument note, with no filler. The Args block is mildly verbose relative to a single parameter but still earns its place by adding format and transport detail.

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?

An output schema exists, so return-value explanation is unnecessary, and the description covers purpose, pipeline position, and input format. Only the absence of any failure-mode or limit note keeps it from being fully complete for a no-annotation tool.

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

Parameters4/5

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

Schema description coverage is 0% and image_path is bare in the schema, so the description must compensate, and it does: it explains that the argument is either a public HTTPS URL (hosted) or a local path (stdio) to a PNG/JPEG/WebP file. The hosting-mode distinction is meaning the schema does not convey.

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?

States a specific verb and resource ('Extract text segments from an image') plus the ordering guarantee ('in reading order (OCR)'). This clearly separates it from siblings like localize_image and translate, which are transformations rather than extraction.

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

Usage Guidelines5/5

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

Explicitly positions the tool as the first step of an image localization pipeline and names the exact sequence: extract, then translate, then re-render with localize_image. When-to-use and which siblings to follow with are both stated, leaving nothing to inference.

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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