dbt

Exposes 20 discovery tools that parse dbt project manifests and artifacts to retrieve models, sources, tests, macros, exposures, and lineage relationships. Uses a discovery client that loads compiled dbt artifacts (manifest.json, catalog.json) and traverses the dependency graph to answer structural queries about project composition, model relationships, and data lineage. Implements pagination and caching strategies to optimize context delivery for large projects.

Provides 10 tools that execute dbt CLI commands (build, run, test, compile, parse, snapshot, seed, freshness, docs generate, retry) by detecting the dbt binary location, validating project structure, and executing commands in isolated subprocess contexts with environment variable injection. Implements CLI binary detection logic that searches system PATH, virtual environments, and project-local installations, then streams command output and exit codes back to the MCP client with error handling and timeout management.

Implements a credential management system that securely stores and retrieves dbt Cloud API tokens, data warehouse credentials, and other authentication secrets. Supports multiple authentication methods including environment variables, credential files, and OAuth flows for dbt Cloud. Uses secure credential storage patterns and implements token refresh logic for OAuth-based authentication. Enables agents to authenticate with dbt Cloud and data warehouses without exposing credentials in tool calls.

Implements a dynamic tool registration system that enables/disables tools based on available credentials and configuration. Tools that require dbt Cloud credentials are automatically disabled if authentication fails; tools requiring data warehouse access are disabled if connection validation fails. Uses a validation framework that tests each tool's prerequisites at startup and during runtime, filtering the tool list exposed to MCP clients based on actual availability.

Implements intelligent caching of dbt artifacts and query results to optimize performance and reduce context size for large projects. Uses pagination tokens to break large result sets into manageable chunks, implements LRU caching for frequently accessed metadata, and provides cache invalidation strategies. Enables agents to work with large dbt projects without overwhelming context windows or causing performance degradation.

Exposes 6 tools that query the dbt Semantic Layer by translating natural language or structured queries into MetricFlow SQL using the Semantic Layer client. Implements a client architecture that authenticates with dbt Cloud, retrieves semantic model definitions (metrics, dimensions, entities), compiles queries to SQL, and executes them against the data warehouse. Supports both direct SQL execution and query compilation for inspection.

Exposes 11 tools that interact with dbt Cloud Admin API to trigger job runs, monitor execution status, retrieve run artifacts, manage job configurations, and query historical run data. Implements an Admin API client that authenticates with dbt Cloud API tokens, constructs API requests, polls for job completion, and parses run artifacts (logs, manifest, run_results.json). Supports async job triggering with status polling and artifact retrieval.

Provides 2 tools that execute raw SQL queries against the dbt data warehouse and translate natural language descriptions into executable SQL. The SQL execution tool connects to the warehouse using dbt profiles and credentials, executes queries with timeout protection, and returns structured results. The translation tool leverages LLM capabilities (via the MCP client) to convert natural language intent into SQL, which can then be executed or inspected.

Exposes 3 tools that generate dbt YAML configurations and SQL templates: source YAML generation from database introspection, model YAML generation with column documentation, and staging model SQL generation. Uses dbt codegen library to introspect database schemas, extract column metadata, and generate boilerplate YAML and SQL that follows dbt best practices. Outputs are ready-to-use templates that reduce manual scaffolding work.

Provides tools that integrate with dbt Fusion Language Server to enable column-level lineage analysis and code intelligence. Uses LSP protocol to query column dependencies, trace data flow through transformations, and provide IDE-like features (hover information, go-to-definition) for dbt SQL code. Requires LSP server running locally or remotely.

Provides tools that search and retrieve dbt product documentation (docs.getdbt.com) to answer questions about dbt features, best practices, and configuration. Implements a documentation search client that indexes dbt docs, supports semantic search, and returns relevant documentation snippets with source URLs. Enables agents to provide context-aware dbt guidance without requiring manual documentation lookup.

Exposes tools that describe the dbt-mcp server itself: available tools, their schemas, authentication requirements, and configuration options. Enables MCP clients to dynamically discover server capabilities, validate tool parameters, and understand authentication flows. Implements introspection endpoints that return tool definitions, required credentials, and supported features.

Enables dbt-mcp to operate on multiple dbt projects within a single workspace by implementing project context switching and isolated tool execution per project. Implements a configuration system that supports multiple project paths, maintains separate artifact caches per project, and routes tool calls to the appropriate project context. Allows agents to work with monorepo dbt setups or multiple independent dbt projects without restarting the server.