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Implements a manifest-based approach that reads the compiled dbt artifact rather than executing dbt commands, enabling fast metadata queries without project recompilation.","intents":["Query dbt project structure and model inventory programmatically","Retrieve project-level statistics like total models, sources, and tests","Access dbt configuration and variable definitions for context in LLM agents","Expose dbt documentation metadata to external tools via MCP protocol"],"best_for":["dbt-core OSS users building LLM agents that need project awareness","Teams integrating dbt metadata into Claude or other MCP-compatible clients","Data engineers automating dbt project analysis and governance"],"limitations":["Requires pre-compiled manifest.json — does not execute dbt commands, so metadata reflects last successful dbt parse/run","No real-time project state — changes to dbt_project.yml require manual dbt parse to update manifest","Limited to dbt-core projects; does not support dbt Cloud-specific features like discovery API"],"requires":["dbt-core 1.0+ with compiled manifest.json artifact","Python 3.8+ for MCP server runtime","MCP-compatible client (Claude Desktop, Cline, etc.)"],"input_types":["file paths (dbt_project.yml, manifest.json)","project directory structure"],"output_types":["JSON structured metadata","text summaries of project configuration"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dbt-docs__cap_1","uri":"capability://data.processing.analysis.model.level.lineage.graph.construction.and.traversal","name":"model-level lineage graph construction and traversal","description":"Reconstructs dbt model dependency graphs from manifest.json by parsing upstream/downstream relationships between models, sources, and tests. Exposes lineage as queryable graph structure enabling traversal of data flow paths, impact analysis, and dependency visualization. Uses manifest node relationships to build directed acyclic graph (DAG) without executing dbt commands.","intents":["Trace data lineage from source tables through transformations to final models","Identify downstream models affected by changes to a specific model","Find upstream sources and dependencies for a given model","Analyze impact of schema changes or source modifications on dependent models"],"best_for":["Data engineers performing impact analysis before model changes","Teams building data governance dashboards that need lineage visualization","LLM agents that need to understand data dependencies for code generation or documentation"],"limitations":["Lineage limited to dbt-managed models and sources — does not include external systems or non-dbt transformations","Requires manifest.json to be up-to-date; stale manifests produce inaccurate lineage","Does not track runtime data flow or actual column-level transformations, only declared dependencies"],"requires":["dbt-core 1.0+ with manifest.json artifact","Python 3.8+ for graph traversal logic","MCP client with tool-calling capability"],"input_types":["model name or source name (string)","direction parameter (upstream/downstream)"],"output_types":["JSON graph structure with nodes and edges","list of related models with relationship types"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dbt-docs__cap_2","uri":"capability://data.processing.analysis.column.level.lineage.and.data.type.tracking","name":"column-level lineage and data type tracking","description":"Extracts column-level lineage information from dbt manifest by parsing model contracts, column definitions, and test metadata. Maps columns through transformation chains to track data types, nullability, and documentation across upstream and downstream models. Implements column-to-column dependency tracking using manifest column metadata and test associations.","intents":["Trace how a specific column transforms through dbt models from source to final table","Identify which columns are tested and what tests apply to them","Verify data type consistency and nullability constraints across model chain","Generate column-level documentation and lineage diagrams for data catalogs"],"best_for":["Data quality teams building column-level lineage for compliance and governance","Teams implementing data contracts and schema validation in dbt","LLM agents generating documentation or SQL based on column-level dependencies"],"limitations":["Column lineage limited to columns explicitly defined in dbt model contracts or tests — inferred columns not tracked","Does not track column transformations at SQL expression level; only model-to-model column mapping","Requires dbt 1.5+ with column-level contracts enabled for full accuracy","Test metadata only available if tests are defined in dbt project; external tests not captured"],"requires":["dbt-core 1.5+ with manifest.json containing column metadata","Models with defined contracts or explicit column documentation","Python 3.8+ for column graph construction"],"input_types":["model name and column name (string)","optional filter for test type or data type"],"output_types":["JSON column lineage graph with data types and nullability","list of tests applied to column","column documentation and descriptions"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dbt-docs__cap_3","uri":"capability://memory.knowledge.dbt.documentation.content.retrieval.and.search","name":"dbt documentation content retrieval and search","description":"Indexes and retrieves dbt documentation content from manifest.json including model descriptions, column documentation, test descriptions, and source definitions. Exposes documentation as searchable text content accessible via MCP tools, enabling LLM agents to cite and reference dbt documentation in responses. Implements text extraction from manifest metadata fields without requiring dbt docs server.","intents":["Retrieve documentation for a specific model, column, or source to include in LLM responses","Search dbt documentation by keyword or pattern to find related models","Access test descriptions and expectations for data quality context","Build documentation-aware LLM agents that cite dbt docs when answering data questions"],"best_for":["Teams using Claude or other LLM clients to ask questions about dbt models","Building AI-powered data documentation assistants","Automating documentation generation or validation workflows"],"limitations":["Documentation limited to text content in manifest — does not include dbt docs website HTML or custom styling","Search is basic text matching; no semantic search or embedding-based retrieval","Markdown formatting in documentation may not be fully preserved in output","Does not track documentation change history or versioning"],"requires":["dbt-core 1.0+ with manifest.json containing documentation metadata","Models and columns with documentation strings defined in dbt project","Python 3.8+ for text extraction and indexing"],"input_types":["model/column/source name (string)","search query (string)","optional filter for documentation type"],"output_types":["documentation text content","JSON with documentation metadata and source references","list of matching models/columns with relevance"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dbt-docs__cap_4","uri":"capability://data.processing.analysis.test.coverage.analysis.and.test.metadata.exposure","name":"test coverage analysis and test metadata exposure","description":"Parses dbt test definitions from manifest.json and maps tests to models and columns they validate. Exposes test metadata including test type (generic/singular), test parameters, and expected outcomes. Enables analysis of test coverage gaps by identifying untested models and columns. Implements test-to-model mapping using manifest test node relationships.","intents":["Identify which models and columns have test coverage and which are untested","Retrieve test definitions and parameters for a specific model","Analyze test coverage gaps and recommend tests for critical models","Generate test coverage reports for data quality governance"],"best_for":["Data quality teams implementing test coverage standards","Teams automating test recommendations for new models","Building data governance dashboards with test coverage metrics"],"limitations":["Only captures tests defined in dbt project — external data quality tools not included","Test execution results not available; only test definitions from manifest","Does not track test failure history or flakiness","Generic test parameters may be complex to parse for all dbt test types"],"requires":["dbt-core 1.0+ with manifest.json containing test definitions","Tests defined in dbt project (tests/ directory or inline)","Python 3.8+ for test metadata parsing"],"input_types":["model name or column name (string)","optional test type filter (generic/singular)"],"output_types":["JSON test metadata with parameters and definitions","test coverage statistics (count of tested vs untested models)","list of tests applied to specific model/column"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dbt-docs__cap_5","uri":"capability://data.processing.analysis.source.and.external.table.metadata.exposure","name":"source and external table metadata exposure","description":"Extracts source definitions from manifest.json including source names, table names, database/schema locations, and source-level documentation. Exposes source metadata as queryable information enabling LLM agents to understand raw data inputs and their properties. Implements source node parsing from manifest with support for source freshness checks and source-level tests.","intents":["Retrieve source table definitions and their database locations","Understand data freshness expectations and source-level tests","Map sources to downstream models that depend on them","Generate documentation about raw data inputs and their quality expectations"],"best_for":["Teams building data lineage tools that need source context","LLM agents answering questions about raw data inputs","Data governance teams tracking external data dependencies"],"limitations":["Source metadata limited to dbt sources.yml definitions — does not connect to actual database schemas","Freshness checks defined in dbt but not executed; only metadata exposed","Does not validate that source tables actually exist in database","External data sources not managed by dbt not captured"],"requires":["dbt-core 1.0+ with manifest.json containing source definitions","Sources defined in sources.yml or equivalent dbt project files","Python 3.8+ for source metadata extraction"],"input_types":["source name and table name (string)","optional filter for source properties"],"output_types":["JSON source metadata with database/schema/table info","source documentation and freshness expectations","list of models depending on source"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dbt-docs__cap_6","uri":"capability://tool.use.integration.mcp.protocol.integration.and.tool.exposure","name":"mcp protocol integration and tool exposure","description":"Implements MCP (Model Context Protocol) server that exposes dbt metadata capabilities as standardized tools callable by MCP-compatible clients (Claude, Cline, etc.). Uses MCP server framework to define tool schemas, handle client requests, and return structured responses. Enables seamless integration of dbt metadata into LLM agent workflows through standard MCP tool-calling interface.","intents":["Make dbt metadata queryable from Claude or other MCP clients via natural language","Enable LLM agents to call dbt metadata tools as part of reasoning chains","Integrate dbt context into multi-step workflows that combine dbt queries with other tools","Expose dbt capabilities to non-technical users through LLM interface"],"best_for":["Teams using Claude Desktop or Cline with MCP support","Building LLM agents that need dbt context in multi-tool workflows","Non-technical users querying dbt metadata through natural language"],"limitations":["Requires MCP-compatible client; does not work with standard REST APIs or direct Python imports","Tool schemas must be pre-defined; dynamic tool generation not supported","Response size limited by MCP protocol and client constraints","No built-in authentication — relies on client-side MCP configuration for security"],"requires":["Python 3.8+ with MCP server library (e.g., mcp package)","MCP-compatible client (Claude Desktop 0.1+, Cline, etc.)","dbt-core 1.0+ with manifest.json artifact"],"input_types":["MCP tool calls with JSON parameters","natural language queries (processed by LLM client)"],"output_types":["JSON structured responses conforming to MCP protocol","text summaries for LLM consumption"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-dbt-docs__cap_7","uri":"capability://data.processing.analysis.manifest.json.parsing.and.caching","name":"manifest.json parsing and caching","description":"Reads and parses dbt manifest.json artifact into in-memory data structures for fast metadata queries. Implements caching of parsed manifest to avoid repeated file I/O and JSON deserialization. Handles manifest schema variations across dbt versions and provides error handling for missing or corrupted manifests. Uses Python JSON parsing with optional caching layer for performance.","intents":["Load dbt project metadata once and serve multiple queries efficiently","Handle manifest updates when dbt parse is re-run","Support multiple dbt projects with separate manifest instances","Provide fast metadata access for interactive LLM agent workflows"],"best_for":["Interactive workflows requiring sub-second metadata queries","Long-running LLM agent sessions that query dbt metadata repeatedly","Multi-project environments with separate manifests"],"limitations":["Manifest must be manually refreshed by running dbt parse; no automatic detection of changes","Large manifests (1000+ models) may consume significant memory","No built-in manifest versioning or rollback capability","Cache invalidation requires server restart or manual cache clear"],"requires":["dbt-core 1.0+ with manifest.json artifact","Python 3.8+ with json module","Sufficient memory for manifest size (typically <100MB for most projects)"],"input_types":["file path to manifest.json","optional cache configuration parameters"],"output_types":["parsed manifest data structures","cache statistics and metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["dbt-core 1.0+ with compiled manifest.json artifact","Python 3.8+ for MCP server runtime","MCP-compatible client (Claude Desktop, Cline, etc.)","dbt-core 1.0+ with manifest.json artifact","Python 3.8+ for graph traversal logic","MCP client with tool-calling capability","dbt-core 1.5+ with manifest.json containing column metadata","Models with defined contracts or explicit column documentation","Python 3.8+ for column graph construction","dbt-core 1.0+ with manifest.json containing documentation metadata"],"failure_modes":["Requires pre-compiled manifest.json — does not execute dbt commands, so metadata reflects last successful dbt parse/run","No real-time project state — changes to dbt_project.yml require manual dbt parse to update manifest","Limited to dbt-core projects; does not support dbt Cloud-specific features like discovery API","Lineage limited to dbt-managed models and sources — does not include external systems or non-dbt transformations","Requires manifest.json to be up-to-date; stale manifests produce inaccurate lineage","Does not track runtime data flow or actual column-level transformations, only declared dependencies","Column lineage limited to columns explicitly defined in dbt model contracts or tests — inferred columns not tracked","Does not track column transformations at SQL expression level; only model-to-model column mapping","Requires dbt 1.5+ with column-level contracts enabled for full accuracy","Test metadata only available if tests are defined in dbt project; external tests not captured","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.037Z","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=dbt-docs","compare_url":"https://unfragile.ai/compare?artifact=dbt-docs"}},"signature":"ztN6WpLSAnpXylp6znkeATSs3rxOl0A8t/4xKY8SMfILKPuX1NNcbLar6p/brBmjYil/26cCs8hgKwSPBKXyCA==","signedAt":"2026-06-22T12:31:37.538Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dbt-docs","artifact":"https://unfragile.ai/dbt-docs","verify":"https://unfragile.ai/api/v1/verify?slug=dbt-docs","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"}}