{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-codebasesearch","slug":"codebasesearch","name":"codebasesearch","type":"mcp","url":"https://github.com/AnEntrypoint/codebasesearch#readme","page_url":"https://unfragile.ai/codebasesearch","categories":["mcp-servers"],"tags":["code-search","embeddings","vector-db","jina","lancedb","mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-codebasesearch__cap_0","uri":"capability://search.retrieval.semantic.code.search.via.embeddings","name":"semantic code search via embeddings","description":"Converts code snippets and natural language queries into dense vector embeddings using Jina's code-aware embedding model, then performs approximate nearest neighbor search against a vector database to find semantically similar code blocks regardless of exact syntax matching. Uses cosine similarity scoring to rank results by semantic relevance rather than keyword overlap, enabling searches like 'authentication middleware' to surface relevant patterns across the codebase.","intents":["Find similar code patterns or implementations across a large codebase without knowing exact syntax","Locate code that solves a specific problem by describing the intent in natural language","Discover reusable functions or modules by semantic similarity rather than naming conventions","Search across multiple files and languages using a single unified semantic index"],"best_for":["developers navigating unfamiliar codebases during onboarding","teams building code reuse libraries and pattern discovery tools","LLM agents that need to ground code generation in existing implementations"],"limitations":["Jina embeddings require network access to embedding API (no offline mode documented)","Semantic search may return false positives for polysemous code patterns (e.g., 'map' function in different contexts)","Embedding quality depends on code documentation and clarity; poorly commented code may have weak semantic signals","No built-in deduplication of near-identical results; requires post-processing for high-precision use cases"],"requires":["Jina API access or self-hosted Jina embedding service","LanceDB 0.3.0+ for vector storage and indexing","Node.js 16+ for MCP server runtime","Codebase files accessible as text (source code, markdown, or plaintext)"],"input_types":["natural language query string","code snippet (any programming language)","file paths to index"],"output_types":["ranked list of code snippets with similarity scores","file paths and line numbers of matches","structured JSON with metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-codebasesearch__cap_1","uri":"capability://data.processing.analysis.codebase.indexing.with.incremental.updates","name":"codebase indexing with incremental updates","description":"Scans a codebase directory, extracts code files (respecting .gitignore patterns), chunks them into semantically meaningful units, generates embeddings for each chunk via Jina, and stores vectors in LanceDB with metadata (file path, line numbers, language). Supports incremental re-indexing to update only changed files rather than full re-embedding, reducing computational overhead on large codebases.","intents":["Build a searchable vector index of an entire codebase for semantic code discovery","Keep the index synchronized with code changes without re-embedding unchanged files","Support multiple programming languages in a single unified index","Enable offline code search after initial indexing"],"best_for":["development teams with large monorepos (10k+ files) needing efficient indexing","CI/CD pipelines that need to update code search indices on every commit","IDE plugins or code editors integrating semantic search without external APIs"],"limitations":["Initial indexing of large codebases (100k+ files) may take hours depending on Jina API rate limits","Chunking strategy not documented; may miss semantic boundaries in complex nested structures","No built-in handling of binary files or non-text formats (images, compiled code)","Incremental updates require tracking file modification times; no automatic change detection from git"],"requires":["Read access to codebase directory structure","Jina API key or self-hosted embedding service","LanceDB installed and initialized","Sufficient disk space for vector database (typically 10-100x source code size)"],"input_types":["file system path to codebase root","optional .gitignore file for exclusion patterns","optional language filter (e.g., 'typescript', 'python')"],"output_types":["LanceDB vector database with embedded code chunks","index metadata (file count, embedding count, last updated timestamp)","status report of indexed files"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-codebasesearch__cap_2","uri":"capability://tool.use.integration.mcp.protocol.server.for.code.search.integration","name":"mcp protocol server for code search integration","description":"Exposes code search capabilities as an MCP (Model Context Protocol) server, allowing Claude, other LLMs, and MCP-compatible clients to invoke semantic code search as a tool within their reasoning loops. Implements MCP resource and tool schemas that map natural language queries to vector search operations, enabling LLM agents to autonomously discover and reference code during code generation or debugging tasks.","intents":["Enable Claude or other LLMs to search a codebase as part of multi-step reasoning or code generation","Integrate code search into LLM-powered code review or refactoring agents","Allow MCP clients to discover relevant code patterns without manual search","Ground LLM code generation in existing codebase patterns and conventions"],"best_for":["teams building LLM agents that need codebase awareness","Claude users wanting to add semantic code search to their conversations","developers integrating code search into MCP-compatible IDEs or tools"],"limitations":["MCP protocol overhead adds ~50-200ms per search request compared to direct library calls","LLM context window limits how many search results can be returned per query (typically 5-20 results)","Requires MCP client support; not compatible with tools that only support REST APIs or direct library imports","No built-in authentication or rate limiting; assumes trusted LLM client environment"],"requires":["MCP client implementation (e.g., Claude desktop app, custom MCP client)","Node.js 16+ for running the MCP server","Configured LanceDB vector index (from indexing capability)","Network connectivity between MCP client and server (local or remote)"],"input_types":["MCP tool call with query string parameter","MCP resource request for codebase metadata"],"output_types":["MCP tool result with ranked code snippets and metadata","MCP resource representation of codebase structure"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-codebasesearch__cap_3","uri":"capability://data.processing.analysis.multi.language.code.chunk.extraction.and.embedding","name":"multi-language code chunk extraction and embedding","description":"Automatically detects programming language from file extension or content, applies language-specific parsing to extract logical code units (functions, classes, methods), and generates embeddings for each unit independently. Preserves language context in embeddings by including language-specific keywords and syntax patterns, enabling Jina's model to understand semantic meaning across Python, JavaScript, TypeScript, Java, Go, Rust, and other languages in a unified vector space.","intents":["Search for similar implementations across different programming languages","Find language-agnostic design patterns or algorithms regardless of syntax","Build a unified search index for polyglot codebases without separate indices per language","Discover cross-language code reuse opportunities or architectural patterns"],"best_for":["teams maintaining microservices or libraries in multiple languages","organizations migrating code between languages and needing pattern discovery","research projects analyzing code patterns across language ecosystems"],"limitations":["Language detection relies on file extensions; may fail for ambiguous or unconventional file naming","Chunking strategy not documented; may split logical units incorrectly for deeply nested or functional code","Jina embeddings may not equally represent all languages; performance varies by language popularity in training data","No support for domain-specific languages (DSLs) or custom syntax extensions"],"requires":["Source code files with standard extensions (.py, .js, .ts, .java, .go, .rs, etc.)","Jina embedding model supporting multi-language code (default model assumed)","LanceDB for storing language-tagged embeddings"],"input_types":["source code files in supported programming languages","optional language hint or override parameter"],"output_types":["language-tagged code chunks with embeddings","metadata including detected language, function/class names, line ranges"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-codebasesearch__cap_4","uri":"capability://search.retrieval.vector.similarity.ranking.with.configurable.thresholds","name":"vector similarity ranking with configurable thresholds","description":"Computes cosine similarity scores between query embeddings and indexed code embeddings, ranks results by similarity score, and filters results based on configurable similarity thresholds. Allows users to tune precision-recall tradeoffs by adjusting minimum similarity scores, enabling strict matching for high-confidence results or relaxed matching for exploratory search.","intents":["Retrieve only highly relevant code matches by setting a high similarity threshold","Explore broader code patterns by lowering the similarity threshold","Tune search behavior for different use cases (strict code review vs exploratory discovery)","Understand confidence levels of search results through similarity scores"],"best_for":["developers needing high-precision code search for critical tasks","exploratory code discovery where recall is more important than precision","automated systems that need tunable confidence thresholds for different workflows"],"limitations":["Similarity thresholds are heuristic; no principled way to set optimal values without domain knowledge","Cosine similarity may not correlate perfectly with human relevance judgments","No built-in ranking by other factors (recency, popularity, test coverage); purely similarity-based","Threshold tuning requires trial-and-error; no adaptive learning from user feedback"],"requires":["Configured similarity threshold parameter (typically 0.0-1.0, default unknown)","Embedded query and code vectors from Jina","LanceDB vector search implementation"],"input_types":["similarity threshold value (float, 0.0-1.0)","optional result limit (max number of results to return)"],"output_types":["ranked list of code snippets with similarity scores","filtered results meeting threshold criteria"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Jina API access or self-hosted Jina embedding service","LanceDB 0.3.0+ for vector storage and indexing","Node.js 16+ for MCP server runtime","Codebase files accessible as text (source code, markdown, or plaintext)","Read access to codebase directory structure","Jina API key or self-hosted embedding service","LanceDB installed and initialized","Sufficient disk space for vector database (typically 10-100x source code size)","MCP client implementation (e.g., Claude desktop app, custom MCP client)","Node.js 16+ for running the MCP server"],"failure_modes":["Jina embeddings require network access to embedding API (no offline mode documented)","Semantic search may return false positives for polysemous code patterns (e.g., 'map' function in different contexts)","Embedding quality depends on code documentation and clarity; poorly commented code may have weak semantic signals","No built-in deduplication of near-identical results; requires post-processing for high-precision use cases","Initial indexing of large codebases (100k+ files) may take hours depending on Jina API rate limits","Chunking strategy not documented; may miss semantic boundaries in complex nested structures","No built-in handling of binary files or non-text formats (images, compiled code)","Incremental updates require tracking file modification times; no automatic change detection from git","MCP protocol overhead adds ~50-200ms per search request compared to direct library calls","LLM context window limits how many search results can be returned per query (typically 5-20 results)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.1763909942679201,"quality":0.2,"ecosystem":0.5800000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:23.901Z","last_scraped_at":"2026-04-22T08:08:13.653Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":381,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=codebasesearch","compare_url":"https://unfragile.ai/compare?artifact=codebasesearch"}},"signature":"zx8+KOTIuzUUPOhsx+6UERAbN+SXSvWouEQjqr+nZ/+agg4lHvCNBjSnYbPcIwJ3FRou3rjNZU3No+rGmLoRBA==","signedAt":"2026-06-21T02:12:08.271Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/codebasesearch","artifact":"https://unfragile.ai/codebasesearch","verify":"https://unfragile.ai/api/v1/verify?slug=codebasesearch","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"}}