{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-sourcerer","slug":"sourcerer","name":"Sourcerer","type":"mcp","url":"https://github.com/st3v3nmw/sourcerer-mcp","page_url":"https://unfragile.ai/sourcerer","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-sourcerer__cap_0","uri":"capability://search.retrieval.semantic.code.search.via.natural.language.queries","name":"semantic code search via natural language queries","description":"Enables AI agents to find relevant code chunks across a codebase using natural language queries rather than regex or file browsing. The system converts user queries into embeddings using OpenAI's embedding API, then performs vector similarity search against a chromem-go vector database containing embeddings of all parsed code chunks. This approach dramatically reduces token consumption by returning only semantically relevant code segments instead of entire files.","intents":["Find a specific function or class by describing what it does in plain English","Locate all code related to a feature without knowing exact file paths or function names","Reduce context window usage by retrieving only relevant code chunks instead of full files","Discover similar implementations across a codebase for refactoring or pattern identification"],"best_for":["AI agents and LLM-based code assistants needing efficient codebase navigation","Teams building semantic code analysis tools with token efficiency constraints","Developers working with large codebases where file-based navigation is inefficient"],"limitations":["Requires OpenAI API key and network connectivity for embedding generation — no offline embedding support currently","Search quality depends on code chunk quality and embedding model capabilities — poor code documentation reduces relevance","Embedding generation adds latency (~500ms-2s per query depending on API load) compared to local regex search","Vector database is in-memory (chromem-go) — no persistence across server restarts without manual export"],"requires":["OpenAI API key with embedding model access (text-embedding-3-small or equivalent)","SOURCERER_WORKSPACE_ROOT environment variable pointing to codebase root","Go runtime for running the MCP server binary"],"input_types":["natural language query string","optional file type filter parameter"],"output_types":["array of code chunks with stable IDs (format: file.ext::Type::method)","semantic similarity scores","source file paths and line numbers"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-sourcerer__cap_1","uri":"capability://code.generation.editing.tree.sitter.based.code.parsing.and.semantic.chunking","name":"tree-sitter based code parsing and semantic chunking","description":"Parses source code using Tree-sitter language parsers to build Abstract Syntax Trees (ASTs), then extracts semantic chunks at the granularity of functions, classes, methods, and interfaces. Each chunk receives a stable ID following the pattern file.ext::Type::method, enabling precise code retrieval and reference. The system supports Go, JavaScript, Python, TypeScript, and Markdown with language-specific extraction rules that respect syntactic boundaries.","intents":["Break down source files into semantically meaningful units for indexing and retrieval","Generate stable, reproducible identifiers for code elements that persist across file edits","Extract language-specific constructs (functions, classes, decorators, interfaces) with their full context","Enable precise code navigation without relying on line numbers that shift with edits"],"best_for":["Multi-language codebases requiring consistent semantic extraction across Go, JavaScript, Python, TypeScript","Systems needing stable code references that survive refactoring and file reorganization","AI agents that need to understand code structure at the semantic level, not just text"],"limitations":["Only 5 languages currently supported (Go, JavaScript, Python, TypeScript, Markdown) — adding new languages requires Tree-sitter grammar and custom extraction logic","Markdown support is limited to sections/headings — not full semantic extraction like code languages","Tree-sitter parsing adds computational overhead (~50-200ms per file depending on size) during initial indexing","Chunk extraction rules are language-specific — no unified abstraction across languages"],"requires":["Tree-sitter language grammars compiled for target languages","Source code in supported language format","Go runtime for parser execution"],"input_types":["source code files in Go, JavaScript, Python, TypeScript, or Markdown format"],"output_types":["semantic chunks with stable IDs","chunk type (function, class, method, interface, decorator, section)","source location (file path, line range)","chunk content (full source code of the element)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-sourcerer__cap_2","uri":"capability://automation.workflow.real.time.file.system.monitoring.with.debounced.indexing","name":"real-time file system monitoring with debounced indexing","description":"Continuously monitors the workspace directory for file changes using file system watchers, detects modifications to source files, and triggers re-indexing of affected chunks with debouncing to avoid redundant parsing during rapid edits. The system respects .gitignore rules to exclude non-source files and maintains a queue of pending files awaiting indexing. This enables the semantic search index to stay synchronized with the codebase without manual refresh commands.","intents":["Keep the semantic search index automatically synchronized with code changes","Avoid re-parsing the entire codebase on every file save by debouncing rapid edits","Exclude build artifacts and dependencies from indexing using .gitignore rules","Track indexing progress and identify files pending processing"],"best_for":["Development workflows where code changes frequently and search results must stay current","Teams using Sourcerer with long-running AI agents that need up-to-date codebase context","Large codebases where full re-indexing on every change would be prohibitively expensive"],"limitations":["Debouncing introduces latency (typically 1-2 seconds) before changes appear in search results — not suitable for real-time collaborative editing scenarios","File watcher behavior is OS-dependent — may miss rapid file deletions or renames on some systems",".gitignore parsing is basic — complex gitignore patterns with negations may not be fully respected","No built-in persistence of pending queue — if server crashes, unprocessed changes are lost"],"requires":["File system watcher support on the host OS (Linux, macOS, Windows)","Read permissions on SOURCERER_WORKSPACE_ROOT and all subdirectories","Valid .gitignore file in repository root (optional but recommended)"],"input_types":["file system events (create, modify, delete)","file paths relative to workspace root"],"output_types":["indexing status (pending, in-progress, complete)","list of files awaiting indexing","updated semantic chunks in vector database"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-sourcerer__cap_3","uri":"capability://tool.use.integration.mcp.protocol.tool.exposure.for.code.discovery","name":"mcp protocol tool exposure for code discovery","description":"Exposes semantic code search and navigation capabilities through the Model Context Protocol (MCP) as callable tools that AI agents can invoke. The system implements five primary MCP tools: semantic_search (natural language queries), get_chunk_code (retrieve by ID), find_similar_chunks (discover related code), index_workspace (manual re-indexing), and get_index_status (progress tracking). This integration allows Claude, other LLMs, and AI agents to treat code discovery as a native capability without custom API integration.","intents":["Enable Claude and other MCP-compatible AI agents to search and navigate code as a built-in tool","Provide agents with precise code retrieval without requiring them to manage file paths or line numbers","Allow agents to discover semantically similar code for refactoring or pattern analysis","Give agents visibility into indexing progress and workspace state"],"best_for":["Teams using Claude or other MCP-compatible AI agents for code analysis and generation","Developers building AI-powered code assistants that need standardized tool interfaces","Organizations standardizing on MCP for AI tool integration across their stack"],"limitations":["MCP protocol overhead adds ~50-100ms per tool invocation compared to direct API calls","Tool parameter validation is basic — no schema enforcement for complex query types","No built-in rate limiting or quota management — agents can spam search requests","Tool responses are JSON-serialized — large code chunks may exceed context window limits if not carefully managed"],"requires":["MCP-compatible client (Claude, custom agent framework, etc.)","Sourcerer MCP server running and accessible to the client","Proper MCP server configuration in client settings"],"input_types":["MCP tool invocation with parameters (query string, chunk IDs, file type filters)"],"output_types":["JSON-formatted tool responses","code chunks with metadata","indexing status objects","similarity scores and rankings"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-sourcerer__cap_4","uri":"capability://search.retrieval.chunk.level.code.retrieval.with.stable.identifiers","name":"chunk-level code retrieval with stable identifiers","description":"Retrieves specific code chunks by their stable IDs (format: file.ext::Type::method) without requiring file path knowledge or line number tracking. The system maintains a mapping from chunk IDs to their source locations and content, enabling precise code access that survives file edits and refactoring. This capability supports both direct ID-based retrieval and discovery of similar chunks through semantic comparison.","intents":["Retrieve a specific function or class by its stable identifier without knowing its file location","Access code chunks that were discovered through semantic search with precise references","Find all semantically similar implementations of a code pattern across the codebase","Build reproducible code references that don't break when files are reorganized"],"best_for":["AI agents that need to reference specific code elements across multiple interactions","Code analysis tools that require stable references to code entities","Refactoring workflows where code locations change but semantic identity persists"],"limitations":["Chunk IDs are generated at parse time — renaming functions or moving code changes the ID, breaking stored references","ID format is opaque to users — no human-readable mapping without consulting the index","Retrieving a chunk returns only that semantic unit — related code in the same file requires separate queries","No support for partial chunk retrieval — always returns the complete function/class/method"],"requires":["Valid chunk ID in format file.ext::Type::method","Chunk must exist in the current index (may be stale if file was recently deleted)"],"input_types":["chunk ID string (file.ext::Type::method format)","array of chunk IDs for batch retrieval"],"output_types":["source code content","source file path","line number range","chunk type and metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-sourcerer__cap_5","uri":"capability://data.processing.analysis.vector.database.indexing.and.embedding.generation","name":"vector database indexing and embedding generation","description":"Builds and maintains a chromem-go in-memory vector database containing embeddings of all parsed code chunks. For each semantic chunk extracted by the parser, the system generates an embedding using OpenAI's embedding API, stores it in the vector database with the chunk ID and metadata, and enables fast similarity search. The database is rebuilt incrementally as files change, with new chunks added and deleted chunks removed from the index.","intents":["Create searchable embeddings of code chunks for semantic similarity matching","Enable fast vector similarity search across thousands of code chunks","Maintain an up-to-date embedding index as code changes","Support semantic search without requiring full-text indexing or regex matching"],"best_for":["Large codebases (1000+ functions) where semantic search is more efficient than file browsing","Teams using OpenAI embeddings and wanting to leverage them for code search","Systems requiring sub-second semantic search latency over code"],"limitations":["In-memory storage (chromem-go) means index is lost on server restart — no persistence layer","Embedding generation cost scales with codebase size — OpenAI API charges per embedding token","Embedding quality depends on OpenAI model capabilities — poor code documentation reduces search relevance","No support for local embedding models — requires OpenAI API key and network connectivity","Vector database size grows linearly with code chunks — large codebases may exceed available RAM"],"requires":["OpenAI API key with embedding model access","Network connectivity to OpenAI API","Sufficient RAM to store all embeddings in memory (typically 100-500MB for large codebases)"],"input_types":["semantic code chunks from parser","chunk metadata (ID, type, file path)"],"output_types":["vector embeddings (1536-dimensional for text-embedding-3-small)","similarity search results ranked by cosine distance","chunk metadata with similarity scores"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-sourcerer__cap_6","uri":"capability://code.generation.editing.multi.language.code.analysis.with.language.specific.extraction","name":"multi-language code analysis with language-specific extraction","description":"Analyzes source code across five programming languages (Go, JavaScript, Python, TypeScript, Markdown) using language-specific Tree-sitter parsers and extraction rules. Each language parser understands language-specific constructs: Go extracts functions/methods/types/interfaces, JavaScript extracts functions/classes/variables, Python extracts functions/classes/decorators, TypeScript extracts functions/interfaces/enums/classes, and Markdown extracts sections/headings. This enables semantically accurate code chunking that respects language idioms and structure.","intents":["Index and search across polyglot codebases without language-specific configuration","Extract language-specific constructs (decorators in Python, interfaces in TypeScript) with proper context","Enable semantic search to work correctly across different programming languages","Support documentation (Markdown) alongside code for comprehensive codebase understanding"],"best_for":["Polyglot teams with codebases spanning multiple languages","Monorepos containing Go services, JavaScript frontends, Python data pipelines, etc.","Organizations wanting unified code search across heterogeneous tech stacks"],"limitations":["Only 5 languages supported — adding new languages requires Tree-sitter grammar and custom extraction logic","Language detection is file-extension-based — no support for polyglot files or non-standard extensions","Extraction rules are hardcoded per language — no user customization of what constitutes a 'chunk'","Markdown support is minimal (sections/headings only) — not equivalent to code language support","No support for language-specific features like Go generics or Python type hints in chunk extraction"],"requires":["Source files with standard extensions (.go, .js, .py, .ts, .md)","Tree-sitter language grammars compiled for each supported language"],"input_types":["source code files in Go, JavaScript, Python, TypeScript, or Markdown"],"output_types":["language-specific semantic chunks","chunk type (function, class, method, interface, decorator, section)","language identifier in chunk metadata"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-sourcerer__cap_7","uri":"capability://automation.workflow.indexing.progress.tracking.and.status.reporting","name":"indexing progress tracking and status reporting","description":"Provides visibility into the indexing state of the workspace through a get_index_status MCP tool that reports current progress, lists files pending indexing, and indicates whether the index is fully synchronized with the file system. The system tracks which files have been parsed, which are queued for processing, and provides status updates without blocking ongoing searches. This enables agents and users to understand index freshness and plan queries accordingly.","intents":["Check whether the semantic search index is up-to-date with recent code changes","Identify which files are pending indexing and estimate time to completion","Understand index freshness before relying on search results for critical decisions","Monitor indexing progress in long-running development sessions"],"best_for":["Development workflows where index freshness matters (e.g., code review, refactoring)","AI agents that need to verify index state before performing code analysis","Teams wanting transparency into background indexing operations"],"limitations":["Status reporting is point-in-time — doesn't predict time to completion for large codebases","No historical tracking of indexing performance — can't identify which files are slow to parse","Pending file list may be stale if files are being modified during status check","No granular per-file status — only aggregate counts of pending/processed files"],"requires":["Sourcerer MCP server running with file watcher active"],"input_types":["no parameters required"],"output_types":["indexing status (idle, in-progress, pending)","count of pending files","count of indexed files","list of files awaiting processing"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-sourcerer__cap_8","uri":"capability://automation.workflow.manual.workspace.re.indexing.trigger","name":"manual workspace re-indexing trigger","description":"Provides an index_workspace MCP tool that allows agents or users to manually trigger a full re-indexing of the workspace, bypassing the automatic file watcher and debouncing logic. This is useful after large code changes, when the file watcher may have missed changes, or when the index becomes corrupted. The re-indexing process parses all source files, generates new embeddings, and rebuilds the vector database from scratch.","intents":["Force a complete index rebuild after large code refactoring or branch switching","Recover from index corruption or inconsistency without restarting the server","Ensure search results are fresh before critical code analysis tasks","Manually synchronize the index after file watcher failures"],"best_for":["Development workflows with large batch changes (e.g., branch merges, major refactoring)","Troubleshooting scenarios where index freshness is suspected","CI/CD pipelines that need to ensure index freshness before running code analysis"],"limitations":["Full re-indexing is computationally expensive — blocks other operations during processing","No progress feedback during re-indexing — clients must wait for completion","Re-indexing the entire codebase regenerates all embeddings, incurring OpenAI API costs","No incremental re-indexing option — always rebuilds the entire index from scratch"],"requires":["Sourcerer MCP server running","Write access to vector database","OpenAI API quota for re-generating all embeddings"],"input_types":["no parameters required"],"output_types":["re-indexing completion status","count of files processed","count of chunks indexed"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with embedding model access (text-embedding-3-small or equivalent)","SOURCERER_WORKSPACE_ROOT environment variable pointing to codebase root","Go runtime for running the MCP server binary","Tree-sitter language grammars compiled for target languages","Source code in supported language format","Go runtime for parser execution","File system watcher support on the host OS (Linux, macOS, Windows)","Read permissions on SOURCERER_WORKSPACE_ROOT and all subdirectories","Valid .gitignore file in repository root (optional but recommended)","MCP-compatible client (Claude, custom agent framework, etc.)"],"failure_modes":["Requires OpenAI API key and network connectivity for embedding generation — no offline embedding support currently","Search quality depends on code chunk quality and embedding model capabilities — poor code documentation reduces relevance","Embedding generation adds latency (~500ms-2s per query depending on API load) compared to local regex search","Vector database is in-memory (chromem-go) — no persistence across server restarts without manual export","Only 5 languages currently supported (Go, JavaScript, Python, TypeScript, Markdown) — adding new languages requires Tree-sitter grammar and custom extraction logic","Markdown support is limited to sections/headings — not full semantic extraction like code languages","Tree-sitter parsing adds computational overhead (~50-200ms per file depending on size) during initial indexing","Chunk extraction rules are language-specific — no unified abstraction across languages","Debouncing introduces latency (typically 1-2 seconds) before changes appear in search results — not suitable for real-time collaborative editing scenarios","File watcher behavior is OS-dependent — may miss rapid file deletions or renames on some systems","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:04.049Z","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=sourcerer","compare_url":"https://unfragile.ai/compare?artifact=sourcerer"}},"signature":"5XEzWZEzLNoPRfISyPUmbnEqDHnh3FbFI4YFrROCbZihulnql7apIaXLCskiM/7dfS2+ksL1qIkbj20n/vejAg==","signedAt":"2026-06-20T14:28:36.492Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sourcerer","artifact":"https://unfragile.ai/sourcerer","verify":"https://unfragile.ai/api/v1/verify?slug=sourcerer","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"}}