{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-xray","slug":"xray","name":"XRAY","type":"mcp","url":"https://github.com/srijanshukla18/xray","page_url":"https://unfragile.ai/xray","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-xray__cap_0","uri":"capability://search.retrieval.progressive.codebase.structure.mapping","name":"progressive-codebase-structure-mapping","description":"Maps project structure and extracts symbols (functions, classes, variables) through directory traversal combined with language-specific AST parsing via ast-grep and Python's native ast module. Returns a hierarchical tree view with optional symbol skeletons showing signatures, enabling AI assistants to understand codebase organization without loading entire files. Uses stateless architecture—no persistent index, analysis happens on-demand per request.","intents":["I need to understand the overall structure of a codebase before diving into specific files","Show me all top-level functions and classes in this project so I can see what's available","Generate a map of the codebase that an AI assistant can use to navigate intelligently"],"best_for":["AI assistants (Claude, Cursor, VS Code) analyzing unfamiliar codebases","Teams onboarding new developers who need rapid codebase orientation","Developers building code-aware AI agents that need structural context"],"limitations":["Stateless design means no persistent caching across requests—each explore_repo call re-traverses the directory tree","Symbol extraction depth limited to top-level declarations; nested class methods require separate find_symbol queries","Large monorepos (10k+ files) may experience latency due to full directory traversal on each request"],"requires":["Python 3.10+","ast-grep-cli >=0.39.0 (binary dependency)","Git repository (for version tracking)","Supported language: Python, JavaScript/TypeScript, or Go"],"input_types":["file system path (string)","optional symbol filter (regex or glob pattern)"],"output_types":["JSON tree structure with file paths and symbol metadata","optional symbol skeletons (function signatures, class definitions)"],"categories":["search-retrieval","code-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xray__cap_1","uri":"capability://search.retrieval.fuzzy.symbol.location.with.structural.ranking","name":"fuzzy-symbol-location-with-structural-ranking","description":"Locates specific functions, classes, or variables across a codebase by combining ast-grep structural search with fuzzy string matching (via thefuzz library). Ranks results by structural relevance (exact matches bubble up) and string similarity, returning symbol objects with precise file locations and line numbers. Handles symbol name variations and typos through fuzzy matching while maintaining structural accuracy via AST queries.","intents":["Find where a specific function is defined so I can understand its implementation","Search for all classes matching a pattern (e.g., 'Handler' or 'Service') across the codebase","Locate a symbol even if I'm not sure of the exact name or spelling"],"best_for":["AI assistants performing targeted code analysis on specific symbols","Developers refactoring code who need to find all usages of a symbol","Teams building code-aware search features for internal tools"],"limitations":["Fuzzy matching adds latency (~50-200ms per query depending on codebase size) due to string similarity computation","Structural search limited to declarations; cannot distinguish between overloaded methods in languages without explicit overload syntax","Ranking algorithm is heuristic-based; very similar symbol names may not rank in expected order"],"requires":["Python 3.10+","ast-grep-cli >=0.39.0","thefuzz >=0.20.0 (fuzzy matching library)","Supported language: Python, JavaScript/TypeScript, or Go"],"input_types":["symbol name (string, partial or exact)","optional language filter (python, javascript, go)"],"output_types":["list of symbol objects with: name, type (function/class/variable), file path, line number, optional signature"],"categories":["search-retrieval","code-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xray__cap_2","uri":"capability://search.retrieval.impact.analysis.via.reference.tracking","name":"impact-analysis-via-reference-tracking","description":"Analyzes where a symbol is referenced across the codebase by using ripgrep for fast text-based search (primary) with Python AST fallback for Python-specific analysis. Returns reference count and precise locations (file, line number) for each usage, enabling AI assistants to understand change impact before refactoring. Stateless design means queries execute on-demand without maintaining a dependency graph.","intents":["Before I rename a function, show me everywhere it's called so I understand the impact","Find all places where a specific class is instantiated or imported","Understand how widely used a particular module or symbol is across the codebase"],"best_for":["Developers performing refactoring who need to assess change scope","AI assistants generating code changes that must update all references","Teams analyzing technical debt by measuring symbol coupling"],"limitations":["Text-based search (ripgrep) cannot distinguish between symbol usage and string literals containing the symbol name—false positives in comments, strings, and unrelated identifiers","Python AST fallback only available for Python code; JS/TS/Go rely on ripgrep text search which is less precise","No semantic understanding of imports/aliases—if a symbol is imported as a different name, references may be missed or require manual filtering","Large codebases (100k+ lines) may experience ripgrep latency; no built-in result pagination"],"requires":["Python 3.10+","ripgrep binary (optional but recommended for performance)","Python ast module (built-in, for Python fallback)","Supported language: Python (with AST fallback), JavaScript/TypeScript, Go (ripgrep only)"],"input_types":["symbol name (string, exact match required)","optional file/directory filter (glob pattern)"],"output_types":["JSON object with: reference count (integer), list of references with file path, line number, and optional context snippet"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xray__cap_3","uri":"capability://tool.use.integration.mcp.protocol.server.integration","name":"mcp-protocol-server-integration","description":"Exposes the three core tools (explore_repo, find_symbol, what_breaks) as MCP (Model Context Protocol) server endpoints via the FastMCP framework. Handles request/response serialization, error handling, and protocol compliance, allowing any MCP-compatible AI assistant (Claude, Cursor, VS Code) to invoke code analysis tools as native functions. Server runs as a subprocess managed by the AI assistant's MCP client configuration.","intents":["I want Claude or Cursor to be able to analyze my codebase without copy-pasting code snippets","Integrate code intelligence into my AI assistant so it can make informed decisions about my project","Set up XRAY as a tool that my AI assistant can call automatically during conversations"],"best_for":["Developers using Claude Desktop, Cursor Editor, or VS Code with MCP support","Teams building custom AI agents that need code analysis capabilities","Organizations integrating XRAY into existing MCP-based AI workflows"],"limitations":["MCP protocol overhead adds ~50-100ms per request due to serialization and subprocess communication","Server is stateless—each request is independent, no session persistence or request batching","Requires MCP client configuration (JSON) to be manually set up; no auto-discovery of XRAY server","FastMCP framework is relatively new; limited ecosystem of debugging tools compared to REST APIs"],"requires":["Python 3.10+","fastmcp >=0.1.0","MCP-compatible AI assistant (Claude Desktop, Cursor, VS Code with MCP extension)","MCP client configuration file (claude_desktop_config.json or equivalent)"],"input_types":["MCP tool call with tool name and parameters (JSON)","tool parameters: repo_path (string), symbol_name (string), etc."],"output_types":["MCP tool result (JSON) with structured response from the underlying tool"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xray__cap_4","uri":"capability://data.processing.analysis.multi.language.ast.parsing.via.tree.sitter","name":"multi-language-ast-parsing-via-tree-sitter","description":"Provides language-agnostic code analysis by leveraging tree-sitter-based AST parsing through the ast-grep binary. Supports Python, JavaScript/TypeScript, and Go with a single unified interface—no language-specific parsers or grammar files required. ast-grep handles language detection via file extension and provides structural queries that work across all supported languages, enabling consistent symbol extraction and search behavior.","intents":["Analyze a polyglot codebase (Python + JS + Go) with a single tool without language-specific configuration","Extract function and class definitions from any supported language using the same API","Build code intelligence features that work across multiple languages without duplicating logic"],"best_for":["Teams with polyglot codebases (microservices, full-stack applications)","Developers building language-agnostic code analysis tools","AI assistants that need to work across multiple programming languages"],"limitations":["Language support limited to Python, JavaScript/TypeScript, and Go—no support for Java, C++, Rust, or other languages","ast-grep binary must be installed separately; no pure Python fallback for all languages","Language detection relies on file extension; ambiguous extensions (e.g., .js for both JavaScript and JSX) may require manual specification","AST query syntax is ast-grep-specific; queries are not portable to other tree-sitter tools"],"requires":["Python 3.10+","ast-grep-cli >=0.39.0 (binary, must be in PATH)","Supported file types: .py, .js, .ts, .jsx, .tsx, .go"],"input_types":["file path or directory path (string)","optional language filter (python, javascript, go)"],"output_types":["AST node objects with type, location, and optional metadata (signature, scope)"],"categories":["data-processing-analysis","code-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xray__cap_5","uri":"capability://automation.workflow.caching.strategy.with.git.aware.invalidation","name":"caching-strategy-with-git-aware-invalidation","description":"Implements a caching layer that stores analysis results (symbol maps, reference indices) with Git-aware invalidation. Cache entries are invalidated when file modification times change or when Git detects new commits, avoiding stale results while minimizing redundant analysis. Caching is transparent to the user—no manual cache management required. Stateless server design means cache is per-request, not global.","intents":["Speed up repeated queries on the same codebase without re-analyzing unchanged files","Ensure that analysis results stay fresh when code is modified or pulled from Git","Reduce latency for AI assistants making multiple queries during a single conversation"],"best_for":["Developers working on large codebases where repeated analysis would be slow","AI assistants making multiple sequential queries on the same codebase","Teams using XRAY in CI/CD pipelines where caching can reduce analysis time"],"limitations":["Cache invalidation is file-level, not symbol-level—modifying a single function invalidates the entire file's symbol cache","Git-aware invalidation requires a .git directory; non-Git repositories fall back to file modification time checks","Cache is stored in memory or on disk (implementation detail); no distributed cache support for multi-machine setups","Cache size is unbounded—no automatic eviction policy for very large codebases analyzed over long sessions"],"requires":["Python 3.10+","Git repository (optional, for Git-aware invalidation)","File system with modification time tracking"],"input_types":["file path or directory path (string)","optional cache TTL parameter (seconds)"],"output_types":["cached analysis result (JSON) or fresh analysis if cache miss"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xray__cap_6","uri":"capability://automation.workflow.stateless.architecture.with.on.demand.analysis","name":"stateless-architecture-with-on-demand-analysis","description":"Implements a stateless server design where each request is analyzed independently without maintaining persistent indices or dependency graphs. Analysis happens on-demand by invoking external tools (ast-grep, ripgrep) per request, avoiding the complexity of index maintenance and synchronization. This design trades per-request latency for operational simplicity—no background indexing, no index corruption, no cache coherency issues.","intents":["I want a code analysis tool that works immediately without waiting for indexing","Avoid the complexity of maintaining and updating a persistent code index","Deploy XRAY without worrying about index synchronization across multiple instances"],"best_for":["Developers who want immediate code analysis without indexing overhead","Teams deploying XRAY in ephemeral environments (containers, serverless)","Projects where code changes frequently and index staleness is a concern"],"limitations":["Each request incurs full analysis cost—no amortization of parsing across multiple queries","Large codebases (100k+ files) may experience noticeable latency (1-5 seconds) per request due to full traversal","No persistent symbol graph—complex queries (e.g., 'find all functions that call this function') require multiple sequential requests","Caching layer (if present) is per-request or per-session, not global—different users/sessions cannot share cached results"],"requires":["Python 3.10+","ast-grep-cli >=0.39.0","ripgrep (optional, for faster text search)"],"input_types":["analysis request with repo path and query parameters"],"output_types":["fresh analysis result (JSON) computed on-demand"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xray__cap_7","uri":"capability://search.retrieval.structural.vs.text.search.hybrid.approach","name":"structural-vs-text-search-hybrid-approach","description":"Provides two complementary search strategies: structural search via ast-grep (understands code syntax and semantics) and text search via ripgrep (fast pattern matching). The tool layer chooses the appropriate strategy based on query type—structural search for symbol definitions and declarations, text search for references and usage patterns. Hybrid approach balances precision (structural) with speed (text) and cross-language support.","intents":["Find where a symbol is defined (structural) vs where it's used (text-based)","Search for code patterns that may not be valid syntax (e.g., comments or string literals)","Understand the trade-offs between precision and speed for different query types"],"best_for":["Developers who need both precise symbol location and fast reference finding","AI assistants performing multi-step code analysis (definition lookup + usage analysis)","Teams analyzing code patterns that span multiple languages"],"limitations":["Structural search (ast-grep) is slower than text search (ripgrep) but more accurate—no automatic optimization to choose the fastest approach","Text search cannot distinguish between symbol usage and string literals—requires manual filtering or post-processing","Structural search only works for supported languages (Python, JS/TS, Go); text search is language-agnostic but less precise","Hybrid approach adds complexity—tool selection logic must be maintained and tested for each query type"],"requires":["Python 3.10+","ast-grep-cli >=0.39.0 (for structural search)","ripgrep (optional, for text search)"],"input_types":["query type (structural or text)","search pattern (string or AST query)"],"output_types":["search results with location information and optional context"],"categories":["search-retrieval","code-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xray__cap_8","uri":"capability://automation.workflow.minimal.dependency.philosophy.with.binary.wrappers","name":"minimal-dependency-philosophy-with-binary-wrappers","description":"Implements XRAY with minimal Python dependencies (only fastmcp and thefuzz) by delegating heavy lifting to external binaries (ast-grep, ripgrep) via subprocess calls. Python code focuses on orchestration and result formatting, not parsing or searching. This design reduces Python package bloat, simplifies dependency management, and leverages battle-tested tools (ast-grep, ripgrep) instead of reimplementing them in Python.","intents":["I want a lightweight Python package that doesn't pull in dozens of transitive dependencies","Use proven tools (ast-grep, ripgrep) instead of reinventing parsing and searching in Python","Deploy XRAY with minimal installation footprint and fast startup time"],"best_for":["Developers who value minimal dependencies and fast installation","Teams deploying XRAY in constrained environments (embedded systems, CI/CD with bandwidth limits)","Projects that want to leverage existing tools (ast-grep, ripgrep) rather than Python alternatives"],"limitations":["Binary dependencies (ast-grep, ripgrep) must be installed separately and available in PATH—no automatic binary installation","Subprocess calls add overhead (~10-50ms per invocation) compared to in-process libraries","Binary compatibility issues may arise on unusual platforms (e.g., ARM, Alpine Linux)—ast-grep and ripgrep may not have pre-built binaries","Debugging subprocess failures requires understanding both Python and the external tool's error messages"],"requires":["Python 3.10+","fastmcp >=0.1.0 (Python dependency)","thefuzz >=0.20.0 (Python dependency)","ast-grep-cli >=0.39.0 (binary, must be in PATH)","ripgrep (optional binary, must be in PATH for text search)"],"input_types":["Python function calls with standard parameters"],"output_types":["structured results from external tools, parsed and formatted by Python"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-xray__cap_9","uri":"capability://tool.use.integration.ai.assistant.integration.via.mcp.config","name":"ai-assistant-integration-via-mcp-config","description":"Provides configuration templates and auto-generation tools for integrating XRAY into AI assistants (Claude Desktop, Cursor, VS Code) via MCP configuration files. Includes an MCP config generator that creates the necessary JSON configuration (claude_desktop_config.json, etc.) with correct paths and parameters. Simplifies the integration process from manual JSON editing to a single command.","intents":["I want to add XRAY to my Claude Desktop setup without manually editing JSON","Generate the correct MCP configuration for my specific environment and Python installation","Integrate XRAY into Cursor or VS Code with minimal setup steps"],"best_for":["Individual developers setting up XRAY for personal use","Teams deploying XRAY across multiple developer machines","Non-technical users who want to integrate XRAY without understanding MCP protocol details"],"limitations":["Config generator assumes standard installation paths; custom Python installations or virtual environments may require manual path adjustment","Generated configuration is static—changes to XRAY installation path require regenerating the config","Different AI assistants (Claude Desktop, Cursor, VS Code) have different config file locations and formats—generator must support all variants","No validation of generated configuration; incorrect paths or parameters will only be caught when the AI assistant tries to invoke XRAY"],"requires":["Python 3.10+","XRAY installed via pip or uv","AI assistant installed (Claude Desktop, Cursor, or VS Code)","Write access to MCP config directory (~/.config/Claude/claude_desktop_config.json or equivalent)"],"input_types":["optional parameters: python_path, xray_path, ai_assistant_type"],"output_types":["MCP configuration JSON file written to the appropriate location"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","ast-grep-cli >=0.39.0 (binary dependency)","Git repository (for version tracking)","Supported language: Python, JavaScript/TypeScript, or Go","ast-grep-cli >=0.39.0","thefuzz >=0.20.0 (fuzzy matching library)","ripgrep binary (optional but recommended for performance)","Python ast module (built-in, for Python fallback)","Supported language: Python (with AST fallback), JavaScript/TypeScript, Go (ripgrep only)","fastmcp >=0.1.0"],"failure_modes":["Stateless design means no persistent caching across requests—each explore_repo call re-traverses the directory tree","Symbol extraction depth limited to top-level declarations; nested class methods require separate find_symbol queries","Large monorepos (10k+ files) may experience latency due to full directory traversal on each request","Fuzzy matching adds latency (~50-200ms per query depending on codebase size) due to string similarity computation","Structural search limited to declarations; cannot distinguish between overloaded methods in languages without explicit overload syntax","Ranking algorithm is heuristic-based; very similar symbol names may not rank in expected order","Text-based search (ripgrep) cannot distinguish between symbol usage and string literals containing the symbol name—false positives in comments, strings, and unrelated identifiers","Python AST fallback only available for Python code; JS/TS/Go rely on ripgrep text search which is less precise","No semantic understanding of imports/aliases—if a symbol is imported as a different name, references may be missed or require manual filtering","Large codebases (100k+ lines) may experience ripgrep latency; no built-in result pagination","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:21.013Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=xray","compare_url":"https://unfragile.ai/compare?artifact=xray"}},"signature":"+jhQxNFpHHuPObvPE654XRYDWhMNuMkxRiIAfWZTREYkA5EVm0l1zKIwDa7JmHKKd5GjJk2DiWPvV5M++zImDw==","signedAt":"2026-06-15T21:06:16.165Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/xray","artifact":"https://unfragile.ai/xray","verify":"https://unfragile.ai/api/v1/verify?slug=xray","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"}}