ocr
Server Details
Arabic-first OCR, translation and document extraction. First call mints a free trial key.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.3/5 across 5 of 5 tools scored.
Each tool serves a clearly distinct purpose: key provisioning, usage checking, OCR, translation, and combined OCR+translation. There is no overlap or ambiguity.
Tool names mostly follow a consistent verb_noun snake_case pattern (e.g., create_api_key, get_usage, ocr_image, translate_text). The one exception is ocr_and_translate, which uses 'and' instead of a verb, but it remains clear and understandable.
With 5 tools, the server covers the core OCR and translation lifecycle without being excessive. Each tool contributes meaningfully, and the scope feels well-calibrated for the domain.
The tool surface covers key provisioning, usage monitoring, OCR, translation, and combined operations. Minor gaps exist, such as missing key deletion or language-specific configuration beyond auto-detection, but core workflows are well-supported.
Available Tools
5 toolscreate_api_keyAInspect
Provision a new Auto-Reader OCR API key instantly, with no human steps. Pass an optional email to unlock the larger free tier (about 250 credits/day, vs about 25/day for an email-less trial key). Store the returned key and pass it as api_key on future calls.
| Name | Required | Description | Default |
|---|---|---|---|
| No | Optional email to attach for the larger free tier and a verification link. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description adds key behavioral details: instant provisioning, no human steps, and email-tied tier differences. It doesn't cover rate limits or response structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise, front-loaded sentences with no unnecessary words. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one optional parameter, no output schema, and no annotations, the description covers key aspects. Mentioning how to use the returned key is good, but response format details are omitted.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter (email) is described in the schema, but the description adds context about unlocking a larger free tier and verification link, enhancing understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provisions a new Auto-Reader OCR API key instantly, differentiating it from siblings like get_usage or ocr_image.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explains when to use (need an API key) and the benefit of providing an email, but doesn't explicitly state when not to use or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_usageAInspect
Check your Auto-Reader OCR key: tier, remaining daily free credits, prepaid credit balance, subscription allowance, and per-minute rate limit. Use it to throttle yourself before hitting a limit.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | Optional key (nsk_live_...). Auto-provisioned if omitted. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses the behavior: it returns specific usage metrics and suggests throttling. No side effects or contradictions are present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with output details and ending with usage advice. Every word is purposeful, making it concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description explicitly lists all returned metrics. Combined with usage guidance, it provides complete context for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter, which already explains its optionality and auto-provisioning. The description adds no additional parameter meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks usage/limits for an Auto-Reader OCR key, listing specific metrics (tier, credits, rate limit). This verb+resource combination distinguishes it from siblings like ocr_image or create_api_key.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises using the tool to throttle before hitting a limit, providing a clear use case. It could be more explicit about when not to use it, but the guidance is sufficient for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ocr_and_translateAInspect
One call: OCR an image, then translate every line into target_lang. Arabic-first OCR and manga-aware Japanese with right-to-left-aware layout, followed by LLM translation. Automatic source-language detection. Provide the image as base64. Ideal for reading foreign documents, signs, manga, or receipts end-to-end in a single step.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | Optional key (nsk_live_...). Auto-provisioned if omitted. | |
| target_lang | Yes | Language to translate into, as a name or code (e.g. English, ar, ja). | |
| image_base64 | Yes | The image encoded as base64 (a data: URI prefix is accepted and stripped). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full burden. It discloses key behaviors: Arabic-first OCR, manga-aware Japanese, right-to-left layout, LLM translation, automatic source detection, and base64 input. Missing details like output format, error handling, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus a short phrase, concise and front-loaded with the key 'One call' concept. No superfluous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the pipeline and use cases but lacks details on output format (translated lines? plain text?) and does not mention limitations (image size, supported languages, or error behavior). With no output schema, the description should cover these aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, but the description adds value for target_lang by giving examples (English, ar, ja) and clarifies base64 input format slightly beyond the schema. For api_key, the description is silent, but schema already covers it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs OCR and translation in one step, specifying verbs 'OCR' and 'translate', the resource 'image', and the target language. It distinguishes from sibling tools like ocr_image and translate_text by emphasizing the combined end-to-end functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear ideal use cases (foreign documents, signs, manga, receipts) and implies when to use this composite tool. However, it does not explicitly contrast with using separate ocr_image and translate_text steps or mention when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ocr_imageAInspect
Extract text from an image with GPU OCR. Best-in-class Arabic (plus Persian/Urdu) accuracy, manga-aware vertical Japanese, and strong English, French, Spanish, German, Chinese, Korean, Russian, Italian and Portuguese — 13+ languages. Automatic language and script detection with lang="auto". Returns reading-order layout text (right-to-left aware, paragraph-gapped) that is ready to feed an LLM or show a human, plus the detected language, the engine used, and the number of text blocks found. Provide the image as base64. Use the mode hint (document | receipt | manga | scene) to tune detection.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Language/script hint. Default "auto" detects it. Codes: ar, fa, ur, en, fr, es, de, ja, zh, ko, ru, it, pt. | auto |
| mode | No | Content hint that tunes detection and prompts. Default "document". | document |
| api_key | No | Optional Auto-Reader OCR key (nsk_live_...). If omitted, a free trial key is auto-provisioned and returned to you in the result. | |
| image_base64 | Yes | The image encoded as base64 (a data: URI prefix is accepted and stripped). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. Discloses returned items: reading-order layout text, detected language, engine used, number of blocks. Notes GPU acceleration and optional API key with auto-provisioning. Could mention rate limits or size constraints, but covers key behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single dense paragraph, front-loaded with core purpose. Every sentence provides useful information. Could be slightly more structured (e.g., bullet points for languages), but no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, no output schema, the description adequately covers input, output, and special capabilities (language support, modes, returned fields). Lacks error handling or limits, but overall complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but description adds meaning: explains 'lang' codes, 'mode' examples, 'api_key' optionality with auto-provisioning, and 'image_base64' format. Adds value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool extracts text from images using GPU OCR, explicitly lists supported languages and features like automatic detection and mode hints. It distinguishes from siblings such as 'translate_text' (translation) and 'ocr_and_translate' (combined OCR+translation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context: use for OCR on images with specific language strengths, automatic detection, and mode hints for document/receipt/manga/scene. Does not explicitly state when not to use it (e.g., for non-images) but implies appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
translate_textAInspect
Translate text between 13+ languages with an LLM. Arabic-first quality, with formality control (formal/informal) and optional context to disambiguate meaning. Handles both short dictionary-style word lookups and full documents. Returns the translation and, when available, alternative phrasings.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to translate. | |
| api_key | No | Optional key (nsk_live_...). Auto-provisioned if omitted. | |
| context | No | Optional background text that improves accuracy (it is not translated). | |
| formality | No | Optional register for the output. | |
| target_lang | Yes | Target language, as a name or code (e.g. English, Arabic, ar, ja, fr). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key traits: Arabic-first quality, formality control, optional context, returns translation and alternatives. However, does not mention rate limits, error handling, or potential biases. Since no annotations are provided, description carries full burden and does so well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, no filler. Efficiently conveys all important information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a 5-parameter tool without output schema. Covers purpose, parameters, and return value. Could mention output format or error conditions, but not necessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage, but description adds value: explains 'Arabic-first quality', notes api_key auto-provisioned, and allows target_lang as name or code. Adds meaning beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Translate text between 13+ languages with an LLM.' The verb 'translate' and resource 'text' are specific. Distinguishes from siblings like 'ocr_and_translate' which handles images.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies general use for translation with formality and context. Does not explicitly state when not to use or recommend alternatives like 'ocr_and_translate' for image-based text.
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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