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Uses tree-sitter AST parsing for syntax-aware chunking across 40+ languages, with LangChain fallback for unsupported syntax.","intents":["Find relevant code functions across a million-line codebase without manual directory navigation","Retrieve semantically similar code patterns to understand architectural patterns","Discover existing implementations before writing new code to avoid duplication","Provide AI coding agents with precise context windows instead of loading entire projects"],"best_for":["AI coding agent developers building on Claude Code, Cursor, or Gemini CLI","Teams managing large monorepos (100K+ lines) where context window efficiency is critical","Organizations using agentic RAG patterns for code generation and refactoring"],"limitations":["Embedding generation latency depends on provider (OpenAI ~500ms per batch, Ollama local but slower)","Vector database synchronization requires periodic re-indexing on large codebases (can take minutes for 1M+ LOC)","Semantic search quality degrades for domain-specific or proprietary code patterns not well-represented in training data","No built-in support for searching across binary files or compiled artifacts"],"requires":["Node.js 18+","API key for at least one embedding provider (OpenAI, VoyageAI, Gemini, or local Ollama instance)","Milvus instance (self-hosted or Zilliz Cloud account)","TypeScript/JavaScript runtime for MCP server"],"input_types":["source code files (JavaScript, TypeScript, Python, Go, Rust, Java, C++, etc.)","natural language queries","code snippets for similarity matching"],"output_types":["ranked list of code snippets with relevance scores","structured metadata (file path, line numbers, function signatures)","embedding vectors for downstream processing"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--claude-context__cap_1","uri":"capability://tool.use.integration.mcp.based.tool.integration.for.ai.coding.assistants","name":"mcp-based tool integration for ai coding assistants","description":"Exposes semantic code search as a Model Context Protocol (MCP) server with standardized tool handlers, enabling Claude Code, Cursor, and other MCP-compatible AI assistants to invoke code search as a native capability without custom integration code. Implements MCP protocol with schema-based function calling and multi-project context management through a unified tool registry.","intents":["Enable Claude Code to search my codebase without leaving the IDE","Let AI agents autonomously discover relevant code during code generation tasks","Integrate code search into agentic workflows without building custom API clients","Support multi-project indexing so agents can search across related codebases"],"best_for":["Claude Code users wanting native codebase search integration","Cursor IDE users building agentic coding workflows","Teams deploying custom AI coding agents that support MCP protocol"],"limitations":["MCP server requires separate process/port management; no built-in process lifecycle management","Tool calling latency adds ~200-500ms per search request due to MCP serialization overhead","Multi-project context switching requires explicit project selection; no automatic context inference","No built-in authentication — relies on environment-based API key management"],"requires":["MCP-compatible AI assistant (Claude Code, Cursor, or custom agent with MCP support)","Node.js 18+ runtime for MCP server","Configuration file specifying project paths and embedding provider credentials"],"input_types":["natural language search queries from AI assistant","code snippets for similarity search","project configuration metadata"],"output_types":["structured tool results with code snippets and metadata","MCP-formatted responses compatible with Claude, Cursor, Gemini"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--claude-context__cap_10","uri":"capability://data.processing.analysis.cost.tracking.and.embedding.provider.analytics","name":"cost tracking and embedding provider analytics","description":"Tracks embedding generation costs, latency, and token usage per provider, providing visibility into indexing expenses and performance. Implements per-provider metrics collection with aggregation by time period and project, enabling cost optimization and provider comparison.","intents":["Understand embedding costs across different providers and models","Identify cost optimization opportunities by comparing provider pricing","Monitor embedding latency to detect performance degradation","Track token usage to forecast monthly costs"],"best_for":["Teams with large indexing budgets wanting cost visibility","Organizations evaluating multiple embedding providers","Cost-conscious teams optimizing infrastructure spend"],"limitations":["Cost tracking requires manual provider pricing configuration","No automatic cost alerts; users must manually review metrics","Metrics storage requires persistent database; no built-in analytics UI","Latency metrics depend on network conditions; not purely provider-dependent"],"requires":["Provider pricing configuration","Persistent storage for metrics (database or file)","Optional: analytics dashboard for visualization"],"input_types":["embedding requests with provider metadata","provider pricing configuration"],"output_types":["cost metrics (total cost, cost per token, cost per file)","latency metrics (p50, p95, p99)","token usage statistics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--claude-context__cap_11","uri":"capability://automation.workflow.configuration.management.with.environment.variable.support","name":"configuration management with environment variable support","description":"Manages system configuration through environment variables, configuration files, and CLI arguments with hierarchical precedence. Supports configuration validation, schema enforcement, and runtime configuration updates without server restart for non-critical settings.","intents":["Configure embedding provider credentials securely via environment variables","Specify project paths and vector database connection strings in config files","Override configuration at runtime for testing and debugging","Validate configuration before starting MCP server"],"best_for":["Teams deploying to multiple environments (dev, staging, prod)","Organizations with strict secret management requirements","Development teams needing flexible configuration for testing"],"limitations":["Configuration validation happens at startup; invalid config blocks server start","No built-in configuration UI; requires manual file editing or CLI","Environment variable precedence can be confusing with multiple sources","No configuration versioning or rollback; changes are immediate"],"requires":["Configuration file (JSON, YAML, or .env format)","Environment variables for sensitive credentials","Node.js 18+ for configuration parsing"],"input_types":["configuration files (JSON, YAML)","environment variables","CLI arguments"],"output_types":["validated configuration object","configuration validation errors"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--claude-context__cap_2","uri":"capability://code.generation.editing.syntax.aware.code.chunking.with.multi.language.ast.parsing","name":"syntax-aware code chunking with multi-language ast parsing","description":"Parses source code using tree-sitter AST parser to identify syntactic boundaries (functions, classes, modules) and chunks code at semantic boundaries rather than fixed line counts. Falls back to LangChain token-based splitting for unsupported languages, preserving code structure and enabling more precise semantic embeddings. Supports 40+ programming languages with language-specific chunking strategies.","intents":["Ensure code embeddings capture complete functions/classes rather than arbitrary line boundaries","Improve semantic search accuracy by preserving syntactic context in embeddings","Support polyglot codebases with consistent chunking across JavaScript, Python, Go, Rust, Java, etc.","Reduce embedding noise by avoiding mid-function splits that confuse semantic search"],"best_for":["Polyglot monorepos with multiple programming languages","Teams prioritizing embedding quality over indexing speed","Projects where function-level code search precision is critical"],"limitations":["Tree-sitter parsing adds ~100-300ms per file depending on file size and language complexity","Fallback to LangChain splitting for unsupported languages may produce lower-quality chunks","Very large functions (>10K LOC) may exceed embedding context limits and require secondary splitting","AST parsing requires language grammar files; custom DSLs not supported without grammar contribution"],"requires":["tree-sitter library and language grammars for target languages","LangChain for fallback token-based splitting","Node.js 18+ with native module support"],"input_types":["source code files in supported languages (JavaScript, TypeScript, Python, Go, Rust, Java, C++, C#, etc.)"],"output_types":["code chunks with metadata (language, function/class name, line ranges)","structured chunks ready for embedding"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--claude-context__cap_3","uri":"capability://automation.workflow.incremental.file.synchronization.with.change.detection","name":"incremental file synchronization with change detection","description":"Monitors filesystem changes using file watchers and Merkle-tree based change detection to identify modified files, avoiding full codebase re-indexing on every change. Implements delta-based synchronization that only re-embeds changed files and updates vector database entries, reducing indexing latency from minutes to seconds for typical code changes.","intents":["Keep codebase index in sync with local file changes without manual re-indexing","Enable real-time code search on actively developed codebases","Reduce indexing cost by only processing changed files instead of entire codebase","Support continuous development workflows where code changes frequently"],"best_for":["Active development teams making frequent code changes","CI/CD pipelines that need to keep search index synchronized with main branch","Solo developers working on large codebases who need instant search updates"],"limitations":["File watcher latency varies by OS (Windows slower than Linux/macOS); may miss rapid successive changes","Merkle-tree computation adds ~50-100ms overhead per sync cycle","Deleted files require explicit cleanup; orphaned embeddings may persist if deletion detection fails","No built-in conflict resolution for concurrent edits across multiple machines"],"requires":["Node.js 18+ with filesystem watcher support","Write access to project directory for change detection","Milvus vector database with update/delete capability"],"input_types":["filesystem events (file create, modify, delete)","project configuration with file patterns to watch"],"output_types":["updated vector database entries","change logs with file paths and operation types"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--claude-context__cap_4","uri":"capability://tool.use.integration.pluggable.embedding.provider.abstraction","name":"pluggable embedding provider abstraction","description":"Abstracts embedding generation behind a provider interface supporting OpenAI, VoyageAI, Gemini, and local Ollama, allowing users to swap embedding models without code changes. Implements provider-specific batching, rate limiting, and fallback strategies, with cost tracking and performance metrics per provider.","intents":["Use OpenAI embeddings for highest quality but pay per token","Switch to VoyageAI for better code-specific embeddings","Run Ollama locally to avoid API costs and latency","Compare embedding quality across providers without code refactoring"],"best_for":["Teams evaluating different embedding models for code search quality","Organizations with strict data residency requirements (prefer local Ollama)","Cost-conscious teams wanting to optimize embedding spend across providers"],"limitations":["Embedding quality varies significantly by provider; no automatic quality detection","Rate limiting differs per provider; batching strategies not portable across providers","Local Ollama requires GPU for acceptable performance; CPU-only inference is slow","No built-in cost estimation; users must manually track API usage"],"requires":["API key for chosen embedding provider (OpenAI, VoyageAI, Gemini) OR local Ollama instance","Configuration specifying provider and model name","Network access to embedding API or local Ollama server"],"input_types":["code chunks (text)","provider configuration (model name, API key, batch size)"],"output_types":["embedding vectors (1536-dim for OpenAI, variable for others)","provider metadata (latency, cost, token usage)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--claude-context__cap_5","uri":"capability://tool.use.integration.vs.code.extension.for.ide.integrated.semantic.code.search","name":"vs code extension for ide-integrated semantic code search","description":"Provides VS Code integration exposing semantic code search through IDE commands and UI panels, enabling developers to search their codebase without leaving the editor. Integrates with the core indexing engine and MCP server, displaying search results with syntax highlighting, file navigation, and one-click code navigation.","intents":["Search my codebase semantically from within VS Code without switching tools","Navigate to relevant code by typing natural language queries","View search results with syntax highlighting and file context","Trigger re-indexing from the IDE without command-line tools"],"best_for":["VS Code users working on large codebases","Teams wanting IDE-native code search without external tools","Developers preferring GUI-based search over CLI"],"limitations":["Extension requires separate MCP server process; no built-in server lifecycle management","Search latency depends on vector database response time; no local caching of results","UI is limited to VS Code; no support for other IDEs (JetBrains, Vim, etc.)","Configuration changes require extension reload; no hot-reload support"],"requires":["VS Code 1.80+","Node.js 18+ for MCP server","Configured MCP server with embedding provider credentials"],"input_types":["natural language search queries typed in VS Code UI","code snippets selected in editor"],"output_types":["search results displayed in VS Code sidebar","clickable file links with line numbers","syntax-highlighted code previews"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--claude-context__cap_6","uri":"capability://tool.use.integration.multi.project.context.management.with.project.switching","name":"multi-project context management with project switching","description":"Manages multiple indexed codebases (projects) within a single MCP server instance, allowing AI agents to switch between projects and search across different codebases. Implements project-scoped vector database collections and configuration management to isolate indexes and prevent cross-project contamination.","intents":["Index multiple related codebases (monorepo + dependencies) and search them independently","Switch AI agent context between projects without restarting the MCP server","Manage separate embedding configurations per project (different providers, models)","Support teams working on multiple projects with shared infrastructure"],"best_for":["Teams managing monorepos with multiple independent services","Organizations with multiple related projects sharing infrastructure","AI agents needing to search across project boundaries"],"limitations":["Project switching requires explicit configuration; no automatic project detection","Vector database must support collection/namespace isolation; not all backends support this equally","Cross-project search not supported; agents must explicitly select project context","Configuration management complexity increases with project count"],"requires":["Milvus vector database with collection support","Project configuration file listing all indexed projects","Separate embedding provider credentials per project (optional)"],"input_types":["project configuration metadata","project selection from AI agent","search queries scoped to specific project"],"output_types":["project-scoped search results","project metadata and status"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--claude-context__cap_7","uri":"capability://automation.workflow.snapshot.based.index.versioning.and.rollback","name":"snapshot-based index versioning and rollback","description":"Captures snapshots of vector database state at specific points in time, enabling rollback to previous index versions if embeddings become corrupted or outdated. Implements snapshot metadata tracking with timestamps and configuration checksums, allowing users to restore to known-good states without full re-indexing.","intents":["Recover from corrupted or stale embeddings by rolling back to previous snapshot","Maintain multiple index versions for A/B testing different embedding models","Track index evolution over time for debugging and auditing","Quickly restore index state after failed re-indexing operations"],"best_for":["Teams with large codebases where re-indexing takes significant time","Organizations requiring audit trails of index changes","Development teams experimenting with different embedding configurations"],"limitations":["Snapshot storage requires additional disk space proportional to vector database size","Rollback operation is not instantaneous; requires vector database restoration time","No automatic snapshot scheduling; manual snapshot creation required","Snapshot metadata not synchronized across distributed deployments"],"requires":["Milvus vector database with backup/restore capability","Persistent storage for snapshot metadata and backups","Manual snapshot creation via CLI or API"],"input_types":["snapshot creation request with optional label","snapshot ID or timestamp for rollback"],"output_types":["snapshot metadata (timestamp, configuration, size)","rollback status and completion time"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--claude-context__cap_8","uri":"capability://data.processing.analysis.file.filtering.and.exclusion.patterns","name":"file filtering and exclusion patterns","description":"Supports configurable file filtering using glob patterns and gitignore-style rules to exclude files from indexing (node_modules, build artifacts, test files, etc.). Implements efficient pattern matching with early termination to avoid parsing excluded files, reducing indexing time and vector database size.","intents":["Exclude node_modules and build artifacts from indexing to reduce index size","Skip test files to focus search on production code","Respect .gitignore patterns automatically without manual configuration","Reduce indexing time by skipping unnecessary files"],"best_for":["Teams with large node_modules or build directories","Projects with extensive test suites that shouldn't be searchable","Organizations wanting to reduce vector database costs by indexing less"],"limitations":["Pattern matching adds ~10-50ms per file depending on pattern complexity","Gitignore integration requires parsing .gitignore files; custom patterns not portable","No dynamic filtering; changes to exclusion patterns require re-indexing","Excluded files cannot be searched; no way to temporarily include them"],"requires":["Configuration file with glob patterns or gitignore-style rules","glob pattern matching library (minimatch or similar)"],"input_types":["file paths","glob patterns or gitignore rules"],"output_types":["boolean (include/exclude decision) per file"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zilliztech--claude-context__cap_9","uri":"capability://search.retrieval.chrome.extension.for.github.code.indexing","name":"chrome extension for github code indexing","description":"Provides browser extension for GitHub that enables semantic code search on public repositories without cloning. Indexes code directly from GitHub's web interface and stores embeddings locally or in cloud backend, enabling semantic search on any public repository.","intents":["Search public GitHub repositories semantically without cloning","Understand unfamiliar codebases by searching for similar patterns","Index open-source dependencies to understand their implementation","Enable semantic code search in GitHub's web UI"],"best_for":["Developers researching open-source projects","Teams understanding third-party dependencies","Researchers analyzing code patterns across GitHub"],"limitations":["Only works on public repositories; private repos require authentication","Indexing happens in browser; large repositories may timeout","No persistent storage by default; indexes lost on browser cache clear","GitHub API rate limiting may prevent indexing large repositories"],"requires":["Chrome/Chromium-based browser","GitHub repository URL","Optional: cloud backend for persistent storage"],"input_types":["GitHub repository URLs","natural language search queries"],"output_types":["search results with file links","GitHub-integrated UI for navigation"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","API key for at least one embedding provider (OpenAI, VoyageAI, Gemini, or local Ollama instance)","Milvus instance (self-hosted or Zilliz Cloud account)","TypeScript/JavaScript runtime for MCP server","MCP-compatible AI assistant (Claude Code, Cursor, or custom agent with MCP support)","Node.js 18+ runtime for MCP server","Configuration file specifying project paths and embedding provider credentials","Provider pricing configuration","Persistent storage for metrics (database or file)","Optional: analytics dashboard for visualization"],"failure_modes":["Embedding generation latency depends on provider (OpenAI ~500ms per batch, Ollama local but slower)","Vector database synchronization requires periodic re-indexing on large codebases (can take minutes for 1M+ LOC)","Semantic search quality degrades for domain-specific or proprietary code patterns not well-represented in training data","No built-in support for searching across binary files or compiled artifacts","MCP server requires separate process/port management; no built-in process lifecycle management","Tool calling latency adds ~200-500ms per search request due to MCP serialization overhead","Multi-project context switching requires explicit project selection; no automatic context inference","No built-in authentication — relies on environment-based API key management","Cost tracking requires manual provider pricing configuration","No automatic cost alerts; users must manually review metrics","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6570312162625771,"quality":0.34,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2026-05-02T15:24:34Z"},"community":{"stars":10626,"forks":780,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=zilliztech--claude-context","compare_url":"https://unfragile.ai/compare?artifact=zilliztech--claude-context"}},"signature":"6Rl+GhQ9AnzrM1aB1r9XZOM+pD5Q40937XNlVXWKRcPAvJYh8ImNyxqRFtdGPFHKoJRwWMCsJjhDTFeD5Uk5Cw==","signedAt":"2026-06-20T18:47:48.482Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/zilliztech--claude-context","artifact":"https://unfragile.ai/zilliztech--claude-context","verify":"https://unfragile.ai/api/v1/verify?slug=zilliztech--claude-context","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"}}