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minheyok-choi

file-analysis-mcp

read_image_text

Read-onlyIdempotent

Extract text from PNG images using OCR. Read wording from screenshots, scanned notes, or labels inside diagrams.

Instructions

PNG 이미지 하나를 OCR로 읽어 텍스트를 추출합니다(Tesseract-OCR 필요).

스크린샷, 스캔한 메모, 다이어그램에 박힌 라벨처럼 이미지 안의 문자를 읽어야 할 때 사용하세요. 순수 그림/사진처럼 텍스트가 없는 이미지는 빈 결과가 나올 수 있습니다(비전 해석이 아니라 OCR이므로 그림 자체의 의미는 파악하지 못합니다).

Returns: ReadImageTextResponse: text에 인식된 문자열이 담깁니다. 결과가 비어 있으면 next_actions에서 그 이유를 안내합니다.

Examples: - 사용: "이 스크린샷에 뭐라고 적혀 있어?" - 사용하지 않음: pdf/docx/pptx/svg 문서 → read_document

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYesOCR로 읽을 PNG 이미지의 절대 경로
max_charsNo반환할 최대 문자 수(컨텍스트 보호용 절단)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
textYes
statusYesOK 또는 PARTIAL 등 처리 결과 상태
metadataYes
truncatedYes
next_actionsNo
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses important behavioral traits: it requires Tesseract-OCR, it is OCR not vision (so it does not understand image meaning), and it may return empty results for text-less images, with next_actions explaining why. This adds significant value over 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 well-structured and front-loaded with the core purpose, followed by usage guidelines, return details, and examples. It is slightly verbose with the examples, but each section carries useful information without redundancy.

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?

For a simple OCR tool, the description covers purpose, usage, limitations, and dependencies. The output schema is mentioned (though not shown) and the max_chars truncation is addressed. The only minor gap is that it could specify what happens on file-not-found or unsupported formats, but these are likely covered elsewhere.

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%, with both parameters clearly described (file_path as absolute path, max_chars as truncation limit). The description itself does not add further parameter detail, so per the calibration baseline of 3 for high coverage, this score is appropriate.

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 specifies the exact verb and resource: 'Reads a PNG image with OCR to extract text' and explicitly distinguishes it from read_document for document formats. It clearly states the tool's scope and what it does not do.

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?

The description explicitly states when to use the tool ('for screenshots, scanned notes, labels on diagrams') and when not to use it ('for pdf/docx/pptx/svg documents → read_document'), providing clear exclusions and routing to alternatives.

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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