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Supports PCRE and JavaScript regex dialects with syntax validation before execution. Returns structured output including match positions, captured groups, and replacement previews for substitution patterns.","intents":["Test regex patterns against sample data before deploying in production code","Extract specific fields from unstructured text using capture groups","Validate regex syntax and identify performance issues with catastrophic backtracking","Preview regex replacements on sample strings before applying to large datasets"],"best_for":["Backend developers building text parsing pipelines","Data validation engineers creating input sanitization rules","LLM agents performing information extraction from documents"],"limitations":["No support for lookahead/lookbehind assertions in some regex dialects","Performance metrics are approximate; actual performance depends on runtime environment","Limited to single-pass matching; no stateful regex engines or context-aware patterns"],"requires":["MCP client with tool-use-integration capability","Valid regex syntax in PCRE or JavaScript dialect","Test string input (can be empty for syntax validation only)"],"input_types":["regex pattern (string)","test string (plain text, can include newlines)","flags (global, case-insensitive, multiline, etc.)"],"output_types":["match array with positions and captured groups","replacement preview (string)","syntax validation report (boolean + error message)","performance warning (boolean + backtracking analysis)"],"categories":["data-processing-analysis","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils__cap_2","uri":"capability://data.processing.analysis.timestamp.conversion.and.timezone.aware.formatting","name":"timestamp conversion and timezone-aware formatting","description":"Converts between Unix timestamps, ISO 8601, and human-readable date formats with full timezone support. Handles daylight saving time transitions, leap seconds, and ambiguous times during DST boundaries. Supports relative time formatting (e.g., '2 hours ago') and batch conversion of timestamp arrays with consistent timezone context.","intents":["Convert API response timestamps to local timezone for display","Generate Unix timestamps from human-readable dates for database queries","Format log timestamps in consistent ISO 8601 for aggregation","Handle timezone-aware scheduling for multi-region deployments"],"best_for":["Backend engineers building time-sensitive APIs and schedulers","DevOps teams analyzing logs across multiple timezones","LLM agents processing temporal data from diverse sources"],"limitations":["Leap second handling depends on system clock; may differ across platforms","Relative time formatting is English-only; no i18n support","DST transition edge cases (2:30 AM during spring forward) require explicit disambiguation"],"requires":["MCP client with tool-use-integration capability","Valid timestamp input (Unix seconds, milliseconds, or ISO 8601 string)","IANA timezone identifier for timezone-aware conversions (e.g., 'America/New_York')"],"input_types":["Unix timestamp (seconds or milliseconds)","ISO 8601 string (with or without timezone)","human-readable date string (parsed with heuristics)","timezone identifier (IANA format)"],"output_types":["Unix timestamp (seconds or milliseconds)","ISO 8601 string (with timezone)","human-readable format (e.g., 'Jan 15, 2024 3:45 PM EST')","relative time string (e.g., '2 hours ago')"],"categories":["data-processing-analysis","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils__cap_3","uri":"capability://data.processing.analysis.identifier.generation.and.validation.with.format.enforcement","name":"identifier generation and validation with format enforcement","description":"Generates and validates identifiers across multiple formats: UUIDs (v1, v4, v5), slugs, nanoids, and custom patterns. Validates existing identifiers against format specifications and detects collisions in batch operations. Supports custom alphabet and length constraints for domain-specific identifier schemes (e.g., API keys, database IDs).","intents":["Generate unique request IDs for distributed tracing and logging","Create URL-safe slugs from arbitrary text for routing and SEO","Validate user-provided identifiers before database insertion","Generate short, collision-resistant IDs for public-facing resources"],"best_for":["Backend developers building APIs with identifier requirements","Database engineers designing primary key strategies","LLM agents generating structured data with unique references"],"limitations":["UUID v5 requires deterministic namespace; non-deterministic if namespace is not stable","Nanoid collision probability increases with batch size; no built-in collision detection for >1M IDs","Custom pattern validation limited to regex; no semantic validation (e.g., checksum verification)"],"requires":["MCP client with tool-use-integration capability","For UUID v5: namespace UUID and name string","For custom patterns: valid regex or alphabet specification"],"input_types":["identifier format type (uuid, slug, nanoid, custom)","text to slugify (for slug generation)","identifier string to validate","custom alphabet and length constraints"],"output_types":["generated identifier (string)","validation result (boolean + error details)","collision report (for batch operations)","format metadata (length, alphabet, pattern)"],"categories":["data-processing-analysis","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils__cap_4","uri":"capability://data.processing.analysis.text.analysis.with.linguistic.metrics.and.pattern.detection","name":"text analysis with linguistic metrics and pattern detection","description":"Analyzes text for linguistic properties including word count, readability scores (Flesch-Kincaid, Gunning Fog), sentiment indicators, and pattern detection (email addresses, URLs, phone numbers). Computes character and token statistics with optional language detection. Returns structured metrics suitable for content quality assessment and automated text classification.","intents":["Assess readability of documentation before publishing","Extract contact information from unstructured text","Detect spam or low-quality content based on linguistic patterns","Estimate token usage for LLM API calls before submission"],"best_for":["Content teams automating quality checks on documentation","Data engineers building text preprocessing pipelines","LLM agents performing content moderation or classification"],"limitations":["Readability scores assume English text; accuracy degrades for other languages","Sentiment detection is rule-based, not ML-based; limited to obvious indicators","Pattern detection (email, URL) uses regex; may have false positives/negatives with edge cases","Token counting is approximate; actual token count depends on tokenizer used by target LLM"],"requires":["MCP client with tool-use-integration capability","Plain text input (supports Unicode)","Optional: language hint for language-specific analysis"],"input_types":["plain text (any length, supports Unicode)","language code (optional, for language-specific metrics)","analysis type selector (readability, sentiment, patterns, all)"],"output_types":["word count, character count, sentence count","readability scores (Flesch-Kincaid grade level, Gunning Fog index)","sentiment indicators (positive/negative/neutral keywords)","extracted patterns (emails, URLs, phone numbers as array)","estimated token count (approximate)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils__cap_5","uri":"capability://data.processing.analysis.json.schema.validation.and.transformation.with.type.coercion","name":"json schema validation and transformation with type coercion","description":"Validates JSON data against JSON Schema specifications with detailed error reporting including path to invalid fields. Supports schema composition (allOf, anyOf, oneOf) and custom format validators. Performs type coercion (string to number, boolean parsing) with configurable strictness levels. Generates sample data matching a schema for testing.","intents":["Validate API request/response payloads before processing","Enforce data contracts between microservices","Coerce user input to expected types with clear error messages","Generate test fixtures matching a schema specification"],"best_for":["API developers building request validation pipelines","Data engineers enforcing schema contracts in ETL workflows","LLM agents validating structured outputs before downstream processing"],"limitations":["Schema composition (allOf, anyOf, oneOf) can be computationally expensive for deeply nested schemas","Type coercion may lose precision (e.g., large integers to floats)","Custom format validators limited to built-in formats; no plugin system for domain-specific validators","Sample generation is random; no support for deterministic or constrained generation"],"requires":["MCP client with tool-use-integration capability","Valid JSON Schema (draft 7 or later recommended)","JSON data to validate (can be partial for partial validation)"],"input_types":["JSON Schema (object)","JSON data to validate (object or array)","coercion mode (strict, lenient, auto)","validation mode (full, partial)"],"output_types":["validation result (boolean)","error array with field paths and messages","coerced data (if coercion enabled)","generated sample data (matching schema)"],"categories":["data-processing-analysis","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils__cap_6","uri":"capability://data.processing.analysis.batch.text.processing.with.parallel.transformation","name":"batch text processing with parallel transformation","description":"Processes multiple text inputs in parallel using transformation functions (uppercase, lowercase, trim, reverse, base64 encode/decode). Applies transformations to arrays of strings with consistent error handling and progress reporting. Supports chaining multiple transformations in sequence with intermediate result inspection.","intents":["Normalize a batch of user inputs (trim whitespace, lowercase)","Encode/decode multiple strings for data migration","Apply consistent text transformations across datasets","Debug text processing pipelines by inspecting intermediate results"],"best_for":["Data engineers building text preprocessing pipelines","Backend developers normalizing user input at scale","LLM agents processing batches of text from APIs"],"limitations":["Parallelization overhead may not be worth it for small batches (<100 items)","Transformation chain errors stop at first failure; no partial success reporting","No support for conditional transformations based on input content","Memory usage scales linearly with batch size; no streaming for very large datasets"],"requires":["MCP client with tool-use-integration capability","Array of strings to process","Valid transformation function names"],"input_types":["array of strings","transformation function (uppercase, lowercase, trim, reverse, base64_encode, base64_decode)","transformation chain (array of functions to apply in sequence)"],"output_types":["array of transformed strings","error array with item indices and error messages","progress report (items processed, items failed)","intermediate results (if inspection enabled)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils__cap_7","uri":"capability://data.processing.analysis.html.xml.parsing.and.extraction.with.xpath.css.selectors","name":"html/xml parsing and extraction with xpath/css selectors","description":"Parses HTML and XML documents and extracts content using XPath expressions or CSS selectors. Returns structured data (text, attributes, nested elements) with optional prettification and validation. Handles malformed HTML gracefully with error recovery. Supports namespace-aware XML parsing for documents with multiple namespaces.","intents":["Extract structured data from HTML pages (tables, lists, metadata)","Parse XML API responses and extract nested elements","Validate HTML/XML structure before processing","Convert HTML to plain text or Markdown for LLM processing"],"best_for":["Web scraping engineers building data extraction pipelines","API developers parsing XML responses from legacy systems","LLM agents extracting information from web content"],"limitations":["XPath/CSS selector performance degrades on very large documents (>10MB)","Namespace handling requires explicit namespace declarations; implicit namespace detection is limited","JavaScript-rendered content not supported; only static HTML/XML","Extraction results are text-based; no semantic understanding of content"],"requires":["MCP client with tool-use-integration capability","Valid HTML or XML document (can be malformed; error recovery enabled)","Valid XPath expression or CSS selector"],"input_types":["HTML or XML document (string)","XPath expression or CSS selector (string)","namespace map (for XML with namespaces, optional)"],"output_types":["extracted text (string or array of strings)","extracted attributes (object or array of objects)","extracted elements (nested structure)","validation report (boolean + error details)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["MCP client compatible with tool-use-integration protocol","Input files with valid UTF-8 or detectable encoding","For CSV: properly delimited source with consistent column counts","MCP client with tool-use-integration capability","Valid regex syntax in PCRE or JavaScript dialect","Test string input (can be empty for syntax validation only)","Valid timestamp input (Unix seconds, milliseconds, or ISO 8601 string)","IANA timezone identifier for timezone-aware conversions (e.g., 'America/New_York')","For UUID v5: namespace UUID and name string","For custom patterns: valid regex or alphabet specification"],"failure_modes":["CSV conversion assumes flat or single-level nesting; deeply nested JSON structures may lose hierarchy","Markdown table conversion limited to tabular data; complex nested structures require manual post-processing","No streaming support for files >100MB; requires full in-memory parsing","No support for lookahead/lookbehind assertions in some regex dialects","Performance metrics are approximate; actual performance depends on runtime environment","Limited to single-pass matching; no stateful regex engines or context-aware patterns","Leap second handling depends on system clock; may differ across platforms","Relative time formatting is English-only; no i18n support","DST transition edge cases (2:30 AM during spring forward) require explicit disambiguation","UUID v5 requires deterministic namespace; non-deterministic if namespace is not stable","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.650433537065026,"quality":0.51,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:18:27.094Z","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=aparajithn-agent-utils","compare_url":"https://unfragile.ai/compare?artifact=aparajithn-agent-utils"}},"signature":"LH0KhYuTTIZ87lEcmFN6c/CiEjcfQJzW5cIrCwn+ylEIJTzZ3jcTcOaMgUoavZpvwEZeGns7WqtsrKU73N5lCA==","signedAt":"2026-06-20T02:00:05.152Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aparajithn-agent-utils","artifact":"https://unfragile.ai/aparajithn-agent-utils","verify":"https://unfragile.ai/api/v1/verify?slug=aparajithn-agent-utils","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"}}