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Glama

ocr

by com.auto-reader

read_manga

OCR a comic/manga page, routed by LANGUAGE (not the blanket "manga = Japanese" assumption). Japanese goes to the manga specialist reader that reads vertical, hand-lettered speech bubbles in right-to-left order; Korean manhwa, Chinese manhua and other scripts use their own OCR pack; low-confidence pages escalate to the vision model. Returns text blocks in reading order plus the detected language, the engine used, and whether the page is vertical. Provide the image as base64. Pass lang explicitly (ko/zh/...) for the best non-Japanese result; default "auto" detects it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage hint. Default "auto". Codes: ja (manga specialist), ko, zh, ar, en, fr, es, de, ru, it, pt.auto
api_keyNoOptional Auto-Reader OCR key (nsk_live_...). If omitted, a free trial key is auto-provisioned and returned to you in the result.
image_base64YesThe manga/comic page as base64 (a data: URI prefix is accepted and stripped).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / lang / description
      Previous value: -"Language hint. Default \"auto\". Codes: ja (manga-ocr), ko, zh, ar, en, fr, es, de, ru, it, pt."New value: +"Language hint. Default \"auto\". Codes: ja (manga specialist), ko, zh, ar, en, fr, es, de, ru, it, pt."
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden, and it delivers: it discloses routing by language, the manga specialist reader, non-Japanese OCR packs, low-confidence escalation to a vision model, and that output includes reading order, detected language, engine, and vertical orientation. It also explains API key auto-provisioning, which is a meaningful side effect.

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 dense but every clause earns its place: purpose, routing, language-specific behavior, escalation, output, and invocation guidance are all packed into a compact paragraph. The most important differentiator (language routing) is front-loaded rather than buried.

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

Completeness5/5

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

For a tool with no output schema and no annotations, the description is unusually complete: it covers input format, optional key behavior, language selection, engine routing, low-confidence handling, and return value specifics. An agent has enough information to select and call this tool correctly without further inference.

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 coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining why the lang parameter matters ('Pass lang explicitly for the best non-Japanese result') and how the default auto-detection behaves. It also reinforces the meaning of image_base64 by framing it as the input format.

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 opens with a precise verb and resource: 'OCR a comic/manga page', then differentiates itself by language routing rather than assuming Japanese. It names the specific audience, engines, and output, which clearly distinguishes it from generic OCR siblings.

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 clearly explains when the tool is appropriate: for comic/manga pages, with language-based routing and explicit handling of Japanese, Korean, Chinese, and other scripts. It does not explicitly name alternative sibling tools like ocr_image, so there is no direct when-not-to-use statement, but the context is strong enough for a capable agent.

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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TDQS

A4.3/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose: key provisioning, structured extraction, usage checking, combined OCR+translation, plain OCR, and translation. No overlap that would confuse an agent.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with underscores (e.g., create_api_key, ocr_image, translate_text). Even the compound name 'ocr_and_translate' fits the pattern.

Tool Count5/5

With 6 tools, the server is well-scoped for an OCR service, covering key management, usage monitoring, multiple OCR modes, structured extraction, and translation without being over-fragmented.

Completeness4/5

The tool set covers core OCR workflows well, but lacks a tool for revoking API keys or listing existing keys, which is a minor gap for full lifecycle management.

Resources