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Implements format-specific codec handlers that validate input and output types, enabling seamless conversion between text representations without manual string manipulation or external library dependencies.","intents":["I need to encode sensitive data for safe transmission in URLs or API payloads","I want to decode Base64-encoded responses from APIs without writing custom parsing logic","I need to convert between hex and ASCII representations for debugging binary data","I'm building an agent that needs to handle multiple encoding formats transparently"],"best_for":["LLM agents processing API responses with encoded payloads","developers building data transformation pipelines","teams automating data sanitization workflows"],"limitations":["No streaming support for large binary files — entire payload must fit in memory","Limited to standard codecs (Base64, URL, hex) — no custom encoding schemes","No automatic format detection — caller must specify source and target encoding explicitly"],"requires":["MCP client compatible with tool calling (Claude, custom agents)","Input strings must be valid UTF-8 or properly formatted binary representations"],"input_types":["text (raw string)","text (Base64-encoded)","text (URL-encoded)","text (hex-encoded)"],"output_types":["text (decoded/encoded string)","structured JSON with encoding metadata"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils-mcp-new__cap_1","uri":"capability://data.processing.analysis.cryptographic.hashing.with.algorithm.selection","name":"cryptographic hashing with algorithm selection","description":"Supports multiple hashing algorithms (MD5, SHA-1, SHA-256, SHA-512, and others) through a parameterized MCP tool that accepts input text and algorithm selection, returning hex-encoded hash digests. 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Implements schema-based validation using standard JSON Schema format, enabling agents to validate API responses, configuration data, or user input without writing custom validation logic.","intents":["Validate API responses conform to expected schema before processing","Check configuration data for required fields and correct types before deployment","Validate user input or form data against schema constraints","Generate detailed error reports for schema violations to guide correction"],"best_for":["agents building data validation pipelines with schema enforcement","teams ensuring API contract compliance across systems","developers validating configuration or input data without custom validators"],"limitations":["Schema validation is strict — no coercion of types (string '123' won't validate as number)","Complex schema features (conditional schemas, references) may not be fully supported","No custom validation rules — limited to standard JSON Schema constraints","Error messages may be verbose for deeply nested schema violations"],"requires":["MCP client with tool-use-integration support","Valid JSON data and JSON Schema definition"],"input_types":["json","structured data"],"output_types":["text","json","validation results"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils-mcp-new__cap_2","uri":"capability://data.processing.analysis.csv.to.json.bidirectional.conversion","name":"csv to json bidirectional conversion","description":"Transforms CSV data into structured JSON arrays of objects (with header-based key mapping) and vice versa, handling delimiter detection, quote escaping, and header normalization. Implements row-by-row parsing that maps CSV columns to JSON object keys, preserving data types where possible and providing options for custom delimiters and quote characters.","intents":["I need to convert CSV exports from databases into JSON for API consumption","I want to transform JSON data structures back into CSV for spreadsheet import","I'm building an agent that processes tabular data from multiple sources with different delimiters","I need to normalize CSV headers (spaces, special characters) into valid JSON keys"],"best_for":["data pipeline builders processing tabular exports","agents integrating spreadsheet data with JSON APIs","teams automating ETL workflows between CSV and JSON formats"],"limitations":["No support for nested JSON structures — output is flat array of objects","Header row is required for CSV-to-JSON conversion — cannot infer structure from data","Large CSV files (>100MB) may cause memory issues — no streaming parser","Type inference is basic (no automatic date/number detection) — all values treated as strings unless explicitly converted","No support for multi-line cell values with embedded newlines in CSV mode"],"requires":["MCP client with tool calling capability","CSV input must have consistent column count across all rows","JSON input must be array of objects with consistent key structure"],"input_types":["text (CSV format with configurable delimiter)","text (JSON array of objects)"],"output_types":["text (JSON array of objects)","text (CSV format)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils-mcp-new__cap_3","uri":"capability://data.processing.analysis.json.to.markdown.table.formatting","name":"json to markdown table formatting","description":"Converts JSON arrays of objects into Markdown table syntax with automatic column width calculation, header formatting, and alignment. Parses JSON structure to extract keys as table headers, iterates through objects to populate rows, and generates properly formatted Markdown with pipe delimiters and alignment indicators.","intents":["I need to format API response data as readable Markdown tables for documentation","I want to convert structured data into GitHub-compatible table format for README files","I'm building an agent that generates reports with formatted data tables","I need to display query results in Markdown format for chat interfaces"],"best_for":["agents generating documentation and reports","developers creating Markdown-based data visualizations","teams automating README and documentation generation"],"limitations":["No support for nested objects — flattens nested structures to string representation","No column width optimization for very long values — may produce wide tables","No sorting or filtering of rows — outputs data in input order","Markdown tables have limited styling — no colors, fonts, or cell merging","Special characters in cell values may break table formatting if not escaped"],"requires":["MCP client with tool calling capability","JSON input must be array of objects with consistent key structure","All object keys must be valid Markdown table column names"],"input_types":["text (JSON array of objects)"],"output_types":["text (Markdown table format)"],"categories":["data-processing-analysis","text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils-mcp-new__cap_4","uri":"capability://data.processing.analysis.html.to.json.structured.data.extraction","name":"html to json structured data extraction","description":"Parses HTML documents and extracts structured data into JSON format using CSS selectors or tag-based queries. Implements DOM traversal to identify elements, extract text content and attributes, and map them to JSON object structures with configurable key naming and nesting.","intents":["I need to scrape HTML tables and convert them to JSON for database import","I want to extract metadata (title, description, links) from web pages as structured data","I'm building an agent that processes HTML email templates and extracts content","I need to parse HTML configuration files and convert them to JSON format"],"best_for":["agents performing web scraping and data extraction","developers building HTML-to-JSON ETL pipelines","teams automating data extraction from HTML reports and documents"],"limitations":["No JavaScript execution — cannot extract data from dynamically rendered content","CSS selector support may be limited — complex selectors may not work as expected","No automatic schema inference — requires explicit selector configuration","Malformed HTML may produce unexpected results — relies on parser error recovery","Large HTML documents (>50MB) may cause performance issues"],"requires":["MCP client with tool calling capability","Valid HTML input (or well-formed XHTML)","CSS selectors or tag names for data extraction"],"input_types":["text (HTML document)"],"output_types":["text (JSON object or array)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils-mcp-new__cap_5","uri":"capability://data.processing.analysis.regex.pattern.testing.and.validation","name":"regex pattern testing and validation","description":"Provides a regex testing tool that accepts a pattern string and input text, returning match results with captured groups, match positions, and validation feedback. Implements regex compilation with error handling, supports multiple regex flavors (JavaScript, Python-compatible), and returns structured results including all captured groups and match metadata.","intents":["I need to test regex patterns against sample data before using them in production code","I want to extract specific parts of text using regex capture groups","I'm building an agent that validates input formats (email, phone, URLs) using regex","I need to debug regex patterns that aren't matching expected text"],"best_for":["developers iterating on regex patterns","agents performing text validation and extraction","teams building input validation pipelines"],"limitations":["No support for lookahead/lookbehind assertions in some regex flavors","Performance degrades with catastrophic backtracking patterns — no timeout protection","Limited to single-line matching by default — multiline mode must be explicitly enabled","No support for named capture groups in all regex flavors","Error messages may be cryptic for complex invalid patterns"],"requires":["MCP client with tool calling capability","Valid regex pattern string","Input text to test against"],"input_types":["text (regex pattern)","text (input string to match)"],"output_types":["structured JSON with match results, captured groups, and positions"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils-mcp-new__cap_6","uri":"capability://data.processing.analysis.text.statistical.analysis.and.metrics","name":"text statistical analysis and metrics","description":"Analyzes text input to compute metrics including word count, character count, sentence count, average word length, readability scores, and keyword frequency distribution. Implements tokenization for word/sentence splitting, calculates linguistic metrics (Flesch-Kincaid grade level, etc.), and returns comprehensive statistics as structured JSON.","intents":["I need to analyze content readability before publishing documentation","I want to extract keyword frequency from documents for SEO optimization","I'm building an agent that evaluates text quality and complexity","I need to generate statistics about text length and composition for reports"],"best_for":["content creators optimizing readability","agents analyzing document quality","teams generating content metrics and analytics"],"limitations":["Readability scores assume English text — may be inaccurate for other languages","Keyword extraction is frequency-based — no semantic understanding or TF-IDF weighting","Sentence detection relies on punctuation — may fail with abbreviated text or special formatting","No support for stemming or lemmatization — counts word forms separately","Performance degrades on very large texts (>10MB)"],"requires":["MCP client with tool calling capability","Text input in UTF-8 encoding"],"input_types":["text (any natural language text)"],"output_types":["structured JSON with word count, character count, readability scores, keyword frequency"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils-mcp-new__cap_7","uri":"capability://data.processing.analysis.configuration.file.parsing.and.extraction","name":"configuration file parsing and extraction","description":"Parses common configuration file formats (YAML, TOML, INI, JSON) and extracts structured data with support for nested keys, environment variable substitution, and validation. Implements format-specific parsers that handle syntax variations, provide error reporting with line numbers, and normalize output to consistent JSON structure.","intents":["I need to read configuration from YAML files and extract specific settings","I want to validate configuration file syntax before deployment","I'm building an agent that manages application settings across multiple config formats","I need to extract environment-specific values from configuration files"],"best_for":["agents managing application configuration","developers building configuration validation tools","teams automating config file processing in CI/CD pipelines"],"limitations":["No support for configuration file comments preservation — comments are stripped","Environment variable substitution is basic — no conditional logic or defaults","No schema validation — cannot enforce required fields or type constraints","Large configuration files (>50MB) may cause memory issues","No support for configuration file merging or inheritance"],"requires":["MCP client with tool calling capability","Valid configuration file in supported format (YAML, TOML, INI, JSON)"],"input_types":["text (YAML, TOML, INI, or JSON configuration)"],"output_types":["structured JSON with parsed configuration values"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils-mcp-new__cap_8","uri":"capability://data.processing.analysis.json.to.html.table.rendering","name":"json to html table rendering","description":"Converts JSON arrays of objects into HTML table markup with configurable styling, column headers, and optional CSS classes. Generates semantic HTML with thead/tbody structure, escapes special characters to prevent injection, and supports custom column ordering and formatting options.","intents":["I need to generate HTML tables from API response data for web display","I want to create styled HTML tables for email reports","I'm building an agent that generates HTML documentation with data tables","I need to convert structured data to HTML for web page embedding"],"best_for":["agents generating HTML reports and dashboards","developers building data visualization tools","teams automating HTML generation for web applications"],"limitations":["No support for nested objects — flattens nested structures to string representation","Limited styling options — basic CSS classes only, no inline styles","No pagination or sorting — outputs entire table in single HTML block","No support for merged cells or complex table structures","Special characters in data must be properly escaped to prevent HTML injection"],"requires":["MCP client with tool calling capability","JSON input must be array of objects with consistent key structure"],"input_types":["text (JSON array of objects)"],"output_types":["text (HTML table markup)"],"categories":["data-processing-analysis","text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_aparajithn-agent-utils-mcp-new__cap_9","uri":"capability://text.generation.language.text.case.transformation.and.normalization","name":"text case transformation and normalization","description":"Provides multiple text transformation functions including uppercase, lowercase, title case, camelCase, snake_case, kebab-case, and PascalCase conversion. Implements language-aware case detection and transformation with support for handling acronyms, numbers, and special characters in different case formats.","intents":["I need to convert variable names between camelCase and snake_case for different languages","I want to normalize text to title case for headers and labels","I'm building an agent that generates code with proper naming conventions","I need to convert between different identifier formats for API compatibility"],"best_for":["agents generating code with language-specific naming conventions","developers automating text normalization","teams building code generation tools"],"limitations":["Acronym handling is basic — may not correctly preserve all-caps sequences","No support for language-specific case rules (e.g., German ß character)","Assumes ASCII input for most transformations — Unicode handling may be incomplete","No context awareness — cannot distinguish between acronyms and regular words"],"requires":["MCP client with tool calling capability","Text input in UTF-8 encoding"],"input_types":["text (any string)"],"output_types":["text (transformed string in requested case format)"],"categories":["text-generation-language","data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["MCP client compatible with tool calling (Claude, custom agents)","Input strings must be valid UTF-8 or properly formatted binary representations","MCP client with tool calling capability","Input text must be valid UTF-8 encoded string","MCP client with tool-use-integration support","Valid JSON data and JSON Schema definition","CSV input must have consistent column count across all rows","JSON input must be array of objects with consistent key structure","All object keys must be valid Markdown table column names","Valid HTML input (or well-formed XHTML)"],"failure_modes":["No streaming support for large binary files — entire payload must fit in memory","Limited to standard codecs (Base64, URL, hex) — no custom encoding schemes","No automatic format detection — caller must specify source and target encoding explicitly","No HMAC support — only simple digest hashing without secret keys","No streaming hash computation — entire input must be loaded into memory","MD5 and SHA-1 included for compatibility but cryptographically weak — not recommended for security-critical applications","Output is always hex-encoded — no option for Base64 or raw binary output","Schema validation is strict — no coercion of types (string '123' won't validate as number)","Complex schema features (conditional schemas, references) may not be fully supported","No custom validation rules — limited to standard JSON Schema constraints","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6341711681552324,"quality":0.47,"ecosystem":0.49000000000000005,"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-mcp-new","compare_url":"https://unfragile.ai/compare?artifact=aparajithn-agent-utils-mcp-new"}},"signature":"AA3MznkADZx6grsLys5aVNjMh8FjGHxM4qRx7vWj3BoHZRIzICH8NlJ49m2BwdjsTwfal5ZkbufM2leLGKwMAQ==","signedAt":"2026-06-20T03:04:30.636Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aparajithn-agent-utils-mcp-new","artifact":"https://unfragile.ai/aparajithn-agent-utils-mcp-new","verify":"https://unfragile.ai/api/v1/verify?slug=aparajithn-agent-utils-mcp-new","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"}}