{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_sharpapi","slug":"sharpapi","name":"SharpAPI","type":"api","url":"https://sharpapi.com","page_url":"https://unfragile.ai/sharpapi","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_sharpapi__cap_0","uri":"capability://text.generation.language.ai.powered.e.commerce.product.description.generation","name":"ai-powered e-commerce product description generation","description":"Generates product descriptions from minimal input (product name, category, attributes) using underlying AI models that synthesize marketing copy optimized for e-commerce platforms. The endpoint accepts structured product metadata and returns human-readable descriptions suitable for catalog listings, leveraging word-quota-based pricing where each generated description consumes a measurable word count against the user's monthly allocation.","intents":["Generate product descriptions at scale for catalog migrations or bulk uploads without manual copywriting","Reduce time spent writing product copy for new SKUs in fast-moving inventory","Maintain consistent tone and style across product listings using AI-assisted generation"],"best_for":["E-commerce teams managing 100+ SKUs who need rapid description generation","Dropshipping businesses scaling product catalogs without in-house copywriters","Marketplace sellers (Amazon, eBay, Shopify) automating bulk listing creation"],"limitations":["No documented input size limits or maximum product attribute complexity — unclear if descriptions are capped at word count","Underlying model name and fine-tuning approach unknown — cannot assess quality consistency across product categories","No streaming support documented — full description must be generated and returned synchronously, blocking on latency","No A/B testing or variant generation capability mentioned — single description per request only"],"requires":["Valid API key from SharpAPI account (free tier: 100k words, paid tiers: 250k–5M words/month)","Product metadata in JSON format (product name, category, attributes minimum)","Sufficient word quota remaining in current billing period"],"input_types":["structured JSON object with product name, category, attributes, price range, target audience"],"output_types":["plain text product description (variable length, counted against monthly word quota)"],"categories":["text-generation-language","e-commerce-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_1","uri":"capability://data.processing.analysis.product.review.sentiment.analysis.with.confidence.scoring","name":"product review sentiment analysis with confidence scoring","description":"Analyzes customer review text to extract sentiment polarity (positive/negative/neutral) and returns a confidence score indicating classification certainty. The implementation uses text classification models to process review content and outputs structured sentiment data that can be aggregated for product quality metrics or used to flag problematic reviews for manual inspection.","intents":["Automatically categorize incoming customer reviews by sentiment to identify product quality issues","Generate sentiment dashboards showing product reputation trends across review sources","Flag low-confidence sentiment classifications for manual review to ensure accuracy"],"best_for":["E-commerce platforms ingesting reviews from multiple sources (Amazon, Trustpilot, native reviews)","Marketplace sellers monitoring brand reputation and competitive positioning","Customer success teams triaging negative reviews for urgent response"],"limitations":["Confidence score calculation method unknown — cannot determine if scores reflect model uncertainty or inter-annotator agreement","No multi-language sentiment support documented — unclear if endpoint handles non-English reviews or requires pre-translation","No aspect-based sentiment capability — only overall review sentiment, not sentiment toward specific product features","Maximum review length not specified — unclear if very long reviews are truncated or processed in full"],"requires":["Valid API key with active word quota (minimum floor charge: $0.01 per job on Build tier)","Review text in plain text or structured format","English language content (language support not documented)"],"input_types":["plain text review content (customer feedback, product reviews)"],"output_types":["structured JSON with sentiment label (positive/negative/neutral) and confidence score (0.0–1.0)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_10","uri":"capability://safety.moderation.profanity.detection.and.content.filtering","name":"profanity detection and content filtering","description":"Identifies profane, offensive, or inappropriate language in text content and flags instances for removal or masking. The implementation uses word-list-based and ML-based profanity detection to identify offensive content, enabling automated content moderation and family-safe content filtering.","intents":["Auto-filter profanity from customer reviews or user-generated content before publishing","Flag offensive language in customer support interactions for escalation or training","Ensure product descriptions and marketing copy are family-safe before publishing"],"best_for":["E-commerce platforms moderating customer reviews for family-safe content","Social media or community platforms filtering user-generated content","Customer support platforms ensuring professional communication standards"],"limitations":["Profanity detection scope not documented — unclear what types of offensive language are detected (slurs, curse words, hate speech, etc.)","No context awareness — cannot distinguish between profanity in quotes, titles, or legitimate uses vs offensive intent","Language support limited — appears to support English only (language support not documented)","No customizable profanity lists — cannot add domain-specific offensive terms or brand-specific language policies","No masking or replacement options — unclear if profanity is flagged, masked, or removed"],"requires":["Valid API key with active word quota (profanity detection counts toward monthly word allocation)","Text content to analyze"],"input_types":["plain text content (reviews, comments, support messages, product descriptions)"],"output_types":["structured JSON with profanity flags, flagged terms, and suggested replacements or masking"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_11","uri":"capability://safety.moderation.ai.generated.content.detection","name":"ai-generated content detection","description":"Analyzes text to determine whether content was generated by AI models or written by humans, returning a classification with confidence score. The implementation uses text analysis models trained to identify statistical patterns and linguistic markers characteristic of AI-generated text, enabling detection of synthetic content for authenticity verification and fraud prevention.","intents":["Detect AI-generated product reviews or fake customer feedback to prevent review fraud","Identify AI-written content in user submissions to enforce authenticity policies","Flag potentially synthetic content in customer communications for verification"],"best_for":["E-commerce platforms preventing fake review fraud and maintaining review authenticity","Content platforms enforcing disclosure of AI-generated content","Marketplace platforms detecting fraudulent seller communications"],"limitations":["Detection accuracy not documented — unclear how well endpoint distinguishes AI-generated vs human-written content","AI model coverage unknown — unclear which AI models the detector is trained to identify (GPT, Claude, Llama, etc.)","No confidence score calibration — unclear if confidence scores reflect true detection certainty","Adversarial robustness unknown — unclear if detector can identify obfuscated or paraphrased AI content","No explanation of detection reasoning — unclear what linguistic markers trigger AI classification"],"requires":["Valid API key with active word quota (AI detection counts toward monthly word allocation)","Text content to analyze"],"input_types":["plain text content (reviews, feedback, comments, product descriptions)"],"output_types":["structured JSON with AI-generated classification (human/AI), confidence score, and detection reasoning"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_12","uri":"capability://data.processing.analysis.email.address.extraction.and.validation","name":"email address extraction and validation","description":"Identifies and extracts email addresses from unstructured text content and validates their format and deliverability. The implementation uses regex-based pattern matching combined with email validation rules to locate email addresses and verify they conform to RFC standards, enabling automated contact data extraction and list cleaning.","intents":["Extract customer email addresses from support tickets or feedback forms for CRM import","Identify and validate email addresses in product reviews or user-generated content","Clean email lists by removing invalid or malformed email addresses"],"best_for":["Customer support teams extracting contact information from unstructured support tickets","Marketing teams building email lists from user-generated content or feedback","Data quality teams validating and cleaning email databases"],"limitations":["Email validation scope not documented — unclear if endpoint validates format only or checks deliverability (SMTP verification)","No duplicate detection — unclear if endpoint identifies and deduplicates multiple instances of same email","No privacy compliance features — unclear if endpoint handles PII masking or GDPR compliance","Maximum input length not specified — unclear if very long documents are truncated","No context awareness — cannot distinguish between email addresses in quotes, examples, or legitimate contact info"],"requires":["Valid API key with active word quota (email extraction counts toward monthly word allocation)","Text content containing email addresses"],"input_types":["plain text content (support tickets, feedback, reviews, product descriptions)"],"output_types":["structured JSON with extracted email addresses and validation status (valid/invalid)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_13","uri":"capability://data.processing.analysis.phone.number.extraction.with.e.164.format.normalization","name":"phone number extraction with e.164 format normalization","description":"Identifies and extracts phone numbers from unstructured text content and normalizes them to E.164 international format (e.g., +1-555-0123). The implementation uses regex-based pattern matching combined with phone number parsing libraries to locate phone numbers in various formats and standardize them for international compatibility.","intents":["Extract customer phone numbers from support tickets or feedback for CRM import","Normalize phone numbers in customer databases to enable international communication","Identify and validate phone numbers in user-generated content for contact list building"],"best_for":["Customer support teams extracting contact information from unstructured support tickets","Global e-commerce platforms normalizing phone numbers across multiple countries","Data quality teams standardizing phone number formats in customer databases"],"limitations":["Country code detection not documented — unclear how endpoint determines country code for numbers without explicit prefix","No phone number validation — unclear if endpoint verifies phone numbers are active or deliverable","No duplicate detection — unclear if endpoint identifies multiple instances of same phone number","Maximum input length not specified — unclear if very long documents are truncated","No context awareness — cannot distinguish between phone numbers in quotes, examples, or legitimate contact info"],"requires":["Valid API key with active word quota (phone extraction counts toward monthly word allocation)","Text content containing phone numbers"],"input_types":["plain text content (support tickets, feedback, reviews, product descriptions)"],"output_types":["structured JSON with extracted phone numbers in E.164 format"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_14","uri":"capability://data.processing.analysis.url.detection.and.extraction.from.unstructured.text","name":"url detection and extraction from unstructured text","description":"Identifies and extracts URLs (hyperlinks) from unstructured text content, including detection of broken or malformed URLs. The implementation uses regex-based URL pattern matching to locate hyperlinks in various formats and validates URL structure to identify potentially broken or suspicious links.","intents":["Extract URLs from customer feedback or support tickets for link analysis or reference","Identify broken links in product descriptions or marketing content for quality assurance","Detect suspicious or malicious URLs in customer communications for security scanning"],"best_for":["Content quality teams identifying broken links in product descriptions or marketing copy","Security teams detecting suspicious URLs in customer communications or user-generated content","SEO teams extracting and analyzing links in customer feedback or reviews"],"limitations":["Broken URL detection method not documented — unclear if endpoint checks link availability or only validates format","No malicious URL detection — unclear if endpoint identifies phishing or malware URLs","No duplicate detection — unclear if endpoint identifies multiple instances of same URL","Maximum input length not specified — unclear if very long documents are truncated","No context awareness — cannot distinguish between URLs in quotes, examples, or legitimate references"],"requires":["Valid API key with active word quota (URL extraction counts toward monthly word allocation)","Text content containing URLs"],"input_types":["plain text content (product descriptions, support tickets, feedback, reviews)"],"output_types":["structured JSON with extracted URLs and validation status (valid/broken)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_15","uri":"capability://data.processing.analysis.address.detection.and.extraction.from.unstructured.text","name":"address detection and extraction from unstructured text","description":"Identifies and extracts physical addresses from unstructured text content, including street addresses, cities, states, and postal codes. The implementation uses regex-based pattern matching combined with address parsing to locate and structure address components, enabling automated contact data extraction and address validation.","intents":["Extract shipping addresses from customer feedback or support tickets for order processing","Identify business locations mentioned in product reviews or customer feedback","Validate and standardize address formats in customer-provided content"],"best_for":["E-commerce platforms extracting shipping addresses from customer communications","Logistics companies identifying delivery locations from customer feedback","Data quality teams standardizing address formats in customer databases"],"limitations":["Address format support not documented — unclear if endpoint handles international addresses or US-only","No address validation — unclear if endpoint verifies addresses are real or deliverable","No duplicate detection — unclear if endpoint identifies multiple instances of same address","No address standardization — unclear if endpoint normalizes address format to USPS or international standards","Maximum input length not specified — unclear if very long documents are truncated"],"requires":["Valid API key with active word quota (address extraction counts toward monthly word allocation)","Text content containing addresses"],"input_types":["plain text content (support tickets, feedback, reviews, product descriptions)"],"output_types":["structured JSON with extracted address components (street, city, state, postal code, country)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_16","uri":"capability://data.processing.analysis.keyword.and.tag.extraction.with.relevance.scoring","name":"keyword and tag extraction with relevance scoring","description":"Identifies and extracts relevant keywords and tags from text content, returning extracted terms with relevance scores indicating importance or frequency. The implementation uses NLP techniques (TF-IDF, topic modeling, or neural embeddings) to identify salient keywords and rank them by relevance, enabling automated content tagging and SEO optimization.","intents":["Auto-generate SEO keywords and tags for product descriptions without manual keyword research","Extract topic keywords from customer feedback to identify common themes or pain points","Generate content tags for product categorization and search optimization"],"best_for":["E-commerce platforms auto-tagging products for search and discovery","Content platforms generating SEO keywords for search engine optimization","Customer insight teams extracting themes from feedback or reviews"],"limitations":["Keyword extraction algorithm not documented — unclear if TF-IDF, topic modeling, or neural embeddings are used","Relevance scoring method unknown — unclear if scores reflect frequency, importance, or other metrics","No customizable keyword lists — cannot specify domain-specific keywords or exclude common terms","No multi-language support documented — appears to support English only","Maximum input length not specified — unclear if very long documents are truncated"],"requires":["Valid API key with active word quota (keyword extraction counts toward monthly word allocation)","Text content to analyze"],"input_types":["plain text content (product descriptions, articles, feedback, reviews)"],"output_types":["structured JSON with extracted keywords/tags and relevance scores"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_17","uri":"capability://text.generation.language.job.description.generation.with.role.customization","name":"job description generation with role customization","description":"Generates job descriptions from minimal input (job title, department, key responsibilities, required skills) using AI models that synthesize professional job postings optimized for recruitment. The endpoint accepts structured job metadata and returns complete job descriptions suitable for posting on job boards, with customizable tone and emphasis on specific qualifications.","intents":["Rapidly generate job descriptions for new open positions without hiring managers writing from scratch","Standardize job description format and content across multiple open positions","Generate role-specific job descriptions for different seniority levels or departments"],"best_for":["HR teams managing high-volume hiring (10+ open positions) requiring rapid job description creation","Recruiting agencies generating job descriptions for multiple clients","Talent acquisition teams standardizing job posting format across organization"],"limitations":["Customization options not documented — unclear what role-specific variations are supported","No compliance validation — unclear if generated descriptions comply with employment law or accessibility standards","No SEO optimization — unclear if descriptions are optimized for job board search algorithms","Maximum input length not specified — unclear if very long responsibility lists are truncated","No quality review process — fully automated generation with no human review option"],"requires":["Valid API key with active word quota (job description generation counts toward monthly word allocation)","Job metadata in JSON format (job title, department, responsibilities, required skills minimum)"],"input_types":["structured JSON object with job title, department, key responsibilities, required skills, preferred qualifications"],"output_types":["plain text job description (variable length, counted against monthly word quota)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_18","uri":"capability://text.generation.language.custom.thank.you.email.generation.for.e.commerce","name":"custom thank-you email generation for e-commerce","description":"Generates personalized thank-you emails for customers after purchase, with customizable tone, product mentions, and promotional offers. The implementation uses template-based generation combined with variable substitution to create personalized emails that reference customer purchase details, enabling automated post-purchase communication without manual email writing.","intents":["Auto-generate personalized thank-you emails after customer purchase to improve retention","Create follow-up emails with product recommendations based on purchase history","Generate promotional offers or loyalty program invitations in thank-you emails"],"best_for":["E-commerce businesses automating post-purchase customer communications","Subscription services sending personalized thank-you emails to new subscribers","Marketplace platforms generating seller thank-you emails to buyers"],"limitations":["Personalization variables not documented — unclear what customer/order data can be substituted (name, product, amount, etc.)","No A/B testing capability — cannot generate multiple email variants for testing","No compliance validation — unclear if generated emails comply with CAN-SPAM or GDPR requirements","No template customization — appears to use fixed email templates with limited customization","No email delivery integration — unclear if endpoint sends emails or returns email content only"],"requires":["Valid API key with active word quota (email generation counts toward monthly word allocation)","Customer and order data in JSON format (customer name, email, product details, order amount minimum)"],"input_types":["structured JSON object with customer name, email, product details, order amount, purchase date"],"output_types":["plain text or HTML email content (variable length, counted against monthly word quota)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_19","uri":"capability://tool.use.integration.restful.api.access.with.word.quota.based.usage.metering","name":"restful api access with word-quota-based usage metering","description":"Provides programmatic access to all SharpAPI endpoints via standard HTTP REST API with JSON request/response format. The implementation uses API key authentication and word-quota-based usage metering where each API call consumes a measurable amount of the user's monthly word allocation, enabling developers to build custom integrations beyond pre-built connectors.","intents":["Build custom applications that integrate SharpAPI capabilities without using pre-built workflow templates","Programmatically call SharpAPI endpoints from internal tools or custom scripts","Integrate SharpAPI with third-party systems not covered by pre-built connectors"],"best_for":["Developers building custom applications requiring AI-powered text processing","Teams with existing internal tools needing AI capability integration","Organizations requiring API-first integration without workflow UI"],"limitations":["API documentation incomplete — no OpenAPI/Swagger spec, request/response schemas, or HTTP status codes documented","Authentication format unknown — unclear if API key is passed as header, query param, or body","No SDK documentation — unclear which languages are supported or how to install SDKs","Error handling not documented — no error response format or retry policy specified","Rate limiting enforcement unclear — API request limits per tier documented but no rate limit headers or backoff guidance"],"requires":["Valid API key from SharpAPI account","HTTP client library (curl, requests, axios, etc.)","Knowledge of REST API conventions (HTTP methods, JSON format)","Active word quota remaining in current billing period"],"input_types":["JSON request body with endpoint-specific parameters"],"output_types":["JSON response with endpoint-specific results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_2","uri":"capability://data.processing.analysis.automated.product.categorization.with.relevance.scoring","name":"automated product categorization with relevance scoring","description":"Classifies products into predefined category taxonomies based on product name, description, and attributes, returning category assignments with relevance scores indicating confidence in each classification. The endpoint uses multi-class or multi-label classification models to map products to hierarchical category structures, enabling automated catalog organization without manual tagging.","intents":["Auto-tag products during bulk imports to eliminate manual category assignment","Standardize product categorization across multiple inventory systems or marketplaces","Identify miscategorized products by filtering for low-confidence categorization scores"],"best_for":["E-commerce platforms managing large product catalogs (1000+ SKUs) with inconsistent categorization","Marketplace aggregators normalizing product data from multiple sellers","Inventory management systems requiring automated product taxonomy mapping"],"limitations":["Supported category taxonomies not documented — unclear if endpoint uses standard taxonomies (Google Shopping, Amazon Browse Nodes) or custom ones","Multi-label vs single-label classification behavior unknown — cannot determine if products can be assigned to multiple categories","No hierarchical category depth specified — unclear if endpoint returns leaf-level categories or intermediate nodes","Category customization not mentioned — appears to use fixed taxonomies with no fine-tuning for domain-specific categories"],"requires":["Valid API key with active word quota","Product metadata (name, description, attributes)","Knowledge of supported category taxonomy structure"],"input_types":["structured JSON with product name, description, attributes, price, brand"],"output_types":["structured JSON with category assignments and relevance scores per category"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_3","uri":"capability://data.processing.analysis.resume.and.cv.parsing.with.structured.data.extraction","name":"resume and cv parsing with structured data extraction","description":"Extracts structured information from resume documents (PDF, DOC, DOCX, TXT, RTF, JPG, PNG, TIFF formats) and returns parsed data points including contact information, work history, education, skills, and certifications. The implementation uses document parsing and NLP to convert unstructured resume text into machine-readable JSON, enabling HR automation workflows like candidate screening and applicant tracking system (ATS) integration.","intents":["Automatically extract candidate data from resumes to populate ATS without manual data entry","Enable resume-based candidate filtering by skills, experience level, or education requirements","Normalize resume data across multiple application sources for consistent candidate comparison"],"best_for":["Recruiting teams processing high-volume applications (100+ resumes/week) requiring rapid data extraction","HR tech platforms integrating resume parsing to reduce manual candidate screening","Talent acquisition automation workflows requiring structured candidate profile creation"],"limitations":["Extracted data points not fully specified — documentation claims 'detailed data points' but exact fields (e.g., GPA, certifications, skills taxonomy) unknown","Multi-language resume support not documented — unclear if non-English resumes are supported or require pre-translation","No confidence scoring on extracted fields — cannot determine extraction accuracy per field type","File size limits not specified — maximum resume file size and page count unknown","No OCR quality guarantees for image-based resumes (JPG, PNG, TIFF) — scanned resume handling quality unknown"],"requires":["Valid API key with active word quota (resume parsing counts toward monthly word allocation)","Resume file in supported format: PDF, DOC, DOCX, TXT, RTF, JPG, JPEG, JPE, PNG, TIFF, TIF","Sufficient word quota for parsing (floor charge: $0.01 per job on Build tier)"],"input_types":["resume document file (PDF, DOC, DOCX, TXT, RTF, JPG, PNG, TIFF formats)"],"output_types":["structured JSON with extracted fields: contact info, work history, education, skills, certifications, summary"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_4","uri":"capability://data.processing.analysis.invoice.parsing.and.structured.financial.data.extraction","name":"invoice parsing and structured financial data extraction","description":"Extracts structured financial data from invoice documents (format support not fully specified) and returns parsed fields including vendor information, line items, amounts, dates, and tax details. The endpoint uses document understanding and OCR to convert invoice images or PDFs into machine-readable JSON, enabling accounts payable automation and expense management workflows.","intents":["Automate invoice data entry into accounting systems without manual line-item transcription","Enable expense categorization and approval routing based on extracted invoice amounts and vendors","Reconcile invoices against purchase orders by matching extracted vendor and line-item data"],"best_for":["Finance teams processing high-volume invoices (500+ monthly) requiring rapid data extraction","Accounting automation platforms integrating invoice parsing for AP workflow optimization","Expense management systems requiring automated invoice-to-payment matching"],"limitations":["Supported invoice formats not documented — unclear if endpoint handles PDF, image, or both","Extracted fields not fully specified — unclear if line-item details (quantity, unit price, tax per line) are extracted or only totals","Multi-currency support not mentioned — unclear if endpoint handles invoices in non-USD currencies","No OCR confidence scoring — cannot determine extraction accuracy for handwritten or low-quality invoice images","No invoice validation or anomaly detection — cannot flag suspicious invoices (e.g., duplicate amounts, mismatched totals)"],"requires":["Valid API key with active word quota","Invoice document in supported format (format details unknown)","Sufficient word quota for parsing"],"input_types":["invoice document (PDF or image format — exact formats not specified)"],"output_types":["structured JSON with extracted fields: vendor info, invoice number, date, line items, subtotal, tax, total amount"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_5","uri":"capability://text.generation.language.multilingual.text.translation.with.style.customization","name":"multilingual text translation with style customization","description":"Translates text content into 80+ languages with support for customizable writing styles and contextual tone adjustments. The implementation uses neural machine translation models with style transfer capabilities, allowing users to specify target tone (formal, casual, technical) and context (e-commerce, legal, marketing) to produce contextually appropriate translations rather than literal word-for-word conversions.","intents":["Localize product descriptions, marketing copy, and customer communications for international markets","Maintain brand voice consistency across translated content by applying style guidelines","Rapidly translate customer support responses in multiple languages without hiring translators"],"best_for":["E-commerce businesses expanding to international markets requiring rapid content localization","Global SaaS platforms automating customer support responses in multiple languages","Content marketing teams producing multilingual campaigns without translation agencies"],"limitations":["Supported languages list not provided — documentation claims '80+ languages' but exact language codes unknown","Style customization options not documented — unclear what writing styles are available or how they're specified","No context-aware terminology handling — unclear if endpoint supports domain-specific glossaries or terminology databases","Maximum input length not specified — unclear if very long documents are truncated or processed in full","No quality assurance or human review option — fully automated translation with no fallback for critical content"],"requires":["Valid API key with active word quota (translation counts toward monthly word allocation)","Source text in supported language (language detection may be automatic)","Target language code (exact format unknown — likely ISO 639-1 or similar)"],"input_types":["plain text content in any supported language"],"output_types":["plain text translation in target language with applied style customization"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_6","uri":"capability://text.generation.language.text.summarization.with.configurable.length.and.detail.level","name":"text summarization with configurable length and detail level","description":"Condenses long-form text content into shorter summaries while preserving key information, with configurable output length and detail level. The implementation uses abstractive or extractive summarization models to identify salient content and generate concise summaries, enabling rapid content consumption and information triage workflows.","intents":["Auto-generate product summary snippets from detailed product descriptions for search results","Create executive summaries of long customer feedback or support tickets for rapid triage","Generate email subject lines or preview text from full email body content"],"best_for":["Content platforms requiring rapid summarization of user-generated content","Customer support teams triaging high-volume tickets by reading AI-generated summaries","E-commerce platforms generating product summary snippets for search and browse pages"],"limitations":["Summarization algorithm not specified — unclear if abstractive (generates new text) or extractive (selects existing sentences)","Configurable length parameters not documented — unclear how to specify target summary length (word count, percentage, etc.)","No multi-document summarization — appears to summarize single documents only, not comparative summaries across multiple texts","No source attribution or citation — unclear if summaries include references to original content sections","Maximum input length not specified — unclear if very long documents are truncated before summarization"],"requires":["Valid API key with active word quota (summarization counts toward monthly word allocation)","Text content to summarize (format and length limits unknown)"],"input_types":["plain text content (product descriptions, articles, support tickets, feedback)"],"output_types":["plain text summary (length and detail level configurable, exact parameters unknown)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_7","uri":"capability://text.generation.language.text.paraphrasing.with.style.and.tone.preservation","name":"text paraphrasing with style and tone preservation","description":"Rewrites text content while maintaining original meaning, with options to preserve or adjust tone, formality level, and writing style. The implementation uses neural language models to generate alternative phrasings that avoid plagiarism while keeping semantic content intact, enabling content reuse and plagiarism avoidance workflows.","intents":["Rewrite product descriptions to avoid duplicate content penalties on search engines","Generate alternative versions of marketing copy for A/B testing without hiring copywriters","Rephrase customer feedback or reviews to create unique content for internal analysis"],"best_for":["E-commerce sellers managing multiple marketplace listings requiring unique product descriptions","Content marketers generating content variations for A/B testing and personalization","SEO teams avoiding duplicate content issues across regional or language-specific sites"],"limitations":["Paraphrasing quality not guaranteed — no documentation on how closely paraphrased text maintains original meaning","Style preservation options not documented — unclear what tone/formality adjustments are available","No plagiarism detection integration — unclear if paraphrased text is validated against plagiarism databases","Maximum input length not specified — unclear if very long documents are truncated","No source attribution — paraphrased content does not include references to original source"],"requires":["Valid API key with active word quota (paraphrasing counts toward monthly word allocation)","Text content to paraphrase"],"input_types":["plain text content (product descriptions, marketing copy, customer feedback)"],"output_types":["plain text paraphrase with adjusted tone/style (exact style options unknown)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_8","uri":"capability://text.generation.language.grammar.checking.and.text.proofreading.with.correction.suggestions","name":"grammar checking and text proofreading with correction suggestions","description":"Analyzes text for grammatical errors, spelling mistakes, punctuation issues, and style improvements, returning flagged errors with correction suggestions and explanations. The implementation uses rule-based and ML-based grammar checking to identify issues and suggest fixes, enabling automated content quality assurance without manual proofreading.","intents":["Auto-correct product descriptions and marketing copy before publishing to eliminate grammar errors","Flag customer support responses for grammar issues before sending to ensure professional communication","Validate user-generated content (reviews, feedback) for language quality before publishing"],"best_for":["E-commerce platforms ensuring product description quality at scale","Customer support teams maintaining professional communication standards","Content platforms moderating user-generated content for language quality"],"limitations":["Grammar checking rules not documented — unclear if endpoint uses standard grammar rules or custom rulesets","Correction suggestion quality not guaranteed — no documentation on accuracy of suggested fixes","Language support limited — appears to support English only (language support not documented)","No style guide customization — cannot enforce brand-specific style guidelines or terminology","Maximum input length not specified — unclear if very long documents are truncated"],"requires":["Valid API key with active word quota (proofreading counts toward monthly word allocation)","Text content to proofread"],"input_types":["plain text content (product descriptions, support responses, user-generated content)"],"output_types":["structured JSON with flagged errors, error types, and correction suggestions"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sharpapi__cap_9","uri":"capability://safety.moderation.spam.detection.with.confidence.scoring.and.explanation","name":"spam detection with confidence scoring and explanation","description":"Classifies text content as spam or legitimate with confidence scores and explanations of detected spam indicators. The implementation uses text classification models trained on spam patterns to identify unwanted content (phishing, promotional spam, malicious links), enabling automated content moderation and security workflows.","intents":["Auto-filter spam emails or customer messages to reduce manual moderation workload","Flag suspicious product reviews or user-generated content for manual inspection","Block phishing or malicious content from reaching customers or support teams"],"best_for":["E-commerce platforms moderating customer reviews and user-generated content","Customer support systems filtering incoming emails and messages","Marketplace platforms preventing fraudulent or malicious seller communications"],"limitations":["Spam detection model not documented — unclear what spam patterns are detected (phishing, promotional, malicious links, etc.)","Confidence score calculation unknown — cannot determine if scores reflect model certainty or other metrics","No customizable spam rules — appears to use fixed spam detection model with no fine-tuning for domain-specific spam patterns","Explanation quality not guaranteed — unclear how detailed spam indicator explanations are","No false positive rate documentation — unclear how often legitimate content is incorrectly flagged as spam"],"requires":["Valid API key with active word quota (spam detection counts toward monthly word allocation)","Text content to analyze"],"input_types":["plain text content (emails, messages, reviews, product descriptions)"],"output_types":["structured JSON with spam classification (spam/legitimate), confidence score, and explanation of detected indicators"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Valid API key from SharpAPI account (free tier: 100k words, paid tiers: 250k–5M words/month)","Product metadata in JSON format (product name, category, attributes minimum)","Sufficient word quota remaining in current billing period","Valid API key with active word quota (minimum floor charge: $0.01 per job on Build tier)","Review text in plain text or structured format","English language content (language support not documented)","Valid API key with active word quota (profanity detection counts toward monthly word allocation)","Text content to analyze","Valid API key with active word quota (AI detection counts toward monthly word allocation)","Valid API key with active word quota (email extraction counts toward monthly word allocation)"],"failure_modes":["No documented input size limits or maximum product attribute complexity — unclear if descriptions are capped at word count","Underlying model name and fine-tuning approach unknown — cannot assess quality consistency across product categories","No streaming support documented — full description must be generated and returned synchronously, blocking on latency","No A/B testing or variant generation capability mentioned — single description per request only","Confidence score calculation method unknown — cannot determine if scores reflect model uncertainty or inter-annotator agreement","No multi-language sentiment support documented — unclear if endpoint handles non-English reviews or requires pre-translation","No aspect-based sentiment capability — only overall review sentiment, not sentiment toward specific product features","Maximum review length not specified — unclear if very long reviews are truncated or processed in full","Profanity detection scope not documented — unclear what types of offensive language are detected (slurs, curse words, hate speech, etc.)","No context awareness — cannot distinguish between profanity in quotes, titles, or legitimate uses vs offensive intent","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:33.096Z","last_scraped_at":"2026-04-05T13:23:42.559Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sharpapi","compare_url":"https://unfragile.ai/compare?artifact=sharpapi"}},"signature":"Hdc/ZJNXWrYk6BiuTCE8Sg06CNLNkX7bbsxb2Go0C6VM4DR9UcaXa0/JC3pyj1+bdrtxHWWlglWSli849Rt7AA==","signedAt":"2026-06-21T03:05:50.655Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sharpapi","artifact":"https://unfragile.ai/sharpapi","verify":"https://unfragile.ai/api/v1/verify?slug=sharpapi","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}