"DuckDB" matching MCP servers:

A minimal MCP server that provides a persistent DuckDB SQL engine to AI assistants, enabling natural-language querying of CSVs, Parquet, and cloud data with 12 tools and optional read-only mode.

A local MCP server enabling AI assistants to query and analyze data via DuckDB SQL engine, supporting local files, memory, S3, and MotherDuck.

A Model Context Protocol server implementation that connects AI assistants to DuckDB, enabling them to query and analyze data from various sources including CSV, Parquet, JSON, and cloud storage through SQL.

A local MCP server implementation that interacts with DuckDB and MotherDuck databases, providing SQL analytics capabilities to AI Assistants and IDEs.

Converts raster and vector geospatial data into H3-indexed Parquet files and serves them via a local MCP server with DuckDB SQL querying.

A unified Model Context Protocol server for data engineering and analytics that lets LLM clients query, profile, transform, and visualize data using DuckDB and PySpark, while keeping raw data out of the model's context through read-only, bounded, and audited operations.

Read-only SQL across PostgreSQL, MySQL, S3-compatible buckets and folders of Parquet, CSV and JSON files — and one query can join across all of them, federated in-process by DuckDB. Sources are given as connection strings at startup, and no tool writes: they are absent rather than disabled.

An MCP server for MotherDuck and local DuckDB.

MCP server for chatting with physical-world data from robotics, drones, automotive, and IoT sources using natural language. It generates auditable SQL queries over Apache Arrow/DuckDB to let you analyze, summarize, and build data pipelines.

Enables AI agents to propose and validate data-cleaning recipes, then receive four-way DuckDB comparisons that separate data, recipe, and interaction effects with exact decimal attribution and row-level provenance.

Enables read-only access to market data and analytics through one vendor-neutral schema, with Alpaca built in and other providers pluggable. Large results are kept out of the model's context in a local DuckDB store the model queries with read-only SQL, and common analytics such as returns, volatility, correlation, drawdown and beta are computed on those stored results.

MCP server for multi-tenant DuckDB management with R2/S3 cloud storage, enabling AI agents to manage per-user databases with automatic cloud persistence.

Open-source agentic schema layer. Define metrics once in YAML, query governed data from any warehouse (Snowflake, BigQuery, Databricks, PostgreSQL, DuckDB) via MCP.

Enables creating interactive data visualizations from natural language queries using DuckDB for local databases or Databricks for enterprise data warehouses. Supports multiple chart types, CSV imports, SQL queries, and automatic statistical analysis through Claude Desktop.

Enables LLM agents to load, explore, and analyze CSV and Excel files using DuckDB, with tools for SQL querying, statistical analysis, expense optimization, and anomaly detection.

MCP server for the OrionBelt Semantic Layer. Enables LLMs to explore governed semantic models, compile YAML metric definitions into optimized SQL across 8 engines (BigQuery, ClickHouse, Databricks, Dremio, DuckDB, MySQL, PostgreSQL, Snowflake), and execute analytics queries via natural language. Works with Claude, Cursor, Windsurf, and Copilot.

Exposes local data profiling, PII classification, and plan-based cleansing against DuckDB, Postgres, or SQLMesh tables through four tools (list_tables, profile, propose_cleansing_plan, apply_cleansing_plan). All reasoning stays aggregate-only — format signatures, counts, and match rates rather than raw cell values — and cleaning plans execute only after validation against a fixed operation vocabulary and explicit confirmation.

Enables LLMs to perform analytical SQL queries on PostgreSQL/MySQL databases via DuckDB, with tools for querying, aggregation, and materialized views.

Provides MCP tools that help AI agents get their bearings in a codebase with unified SQL views over code, git, docs, and conversations, powered by DuckDB.

Provides data preparation, anonymization, and processing tools for AI agents, handling big data with Polars and DuckDB.