Enables AI agents to index and search across SQLite databases and CSV files to discover table schemas and column metadata. It provides a unified MCP API for data source management and structural exploration through natural language.
Enables AI assistants to interact with DBT (Data Build Tool) projects, allowing them to query project metadata, inspect models and sources, view compiled SQL, and run DBT commands.
Enables AI clients to automatically investigate dbt test failures, trace root cause through BigQuery lineage, and generate plain-English incident reports.
A read-only MCP server that exposes dbt project artifacts and data quality result tables (BigQuery/Postgres) to LLM clients, enabling deep introspection, run-history analysis, source freshness, test coverage, and lineage walks.
A Model Context Protocol server for managing and executing dbt Cloud jobs via Claude, with search, run history, and job triggering capabilities, including safety confirmations.
A server that enables querying the dbt Semantic Layer through natural language conversations with Claude Desktop and other AI assistants, allowing users to discover metrics, create queries, analyze data, and visualize results.
A production-ready MCP server that provides comprehensive dbt project quality assessment for any GitHub repository, enabling AI agents to analyze dbt models, check metadata coverage, and map data lineage.
Enables AI agents to discover, request access to, and query data products in Data Mesh Manager, enforcing governance policies while retrieving business data from platforms like Snowflake and Databricks.
An MCP server for dataset exploration and analysis, enabling LLM clients to perform summary, correlation, distribution, missing value analysis, data cleaning, and statistical tests directly on CSV files.
Bootstrap MCP server for future data exploration and querying across multiple databases, currently only provides a hello_world tool with no actual data connectivity.
Enables the analysis of DBT manifests with automatic schema version detection and lineage tracking, allowing users to query model dependencies, access compiled code, and get detailed model information.
An MCP server that gives AI assistants the ability to connect to, query, profile, and monitor data sources — turning any LLM into an interactive data engineering copilot.
Enables conversational analysis of CSV and Parquet files through natural language, providing statistics, summaries, data type information, and comprehensive multi-step data analysis.