"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.

Enables read-only SQL querying and exploration of data files (CSV, Parquet, JSON, Excel, etc.) via DuckDB, supporting local paths, globs, URLs, and S3 buckets.

A read-only DuckDB MCP server offering context-efficient analytics tools (list_datasets, describe_table, profile_column, explain, query) with a semantic layer for business rules, security guards, and disclosed truncation to help LLMs produce correct answers while minimizing token usage.

Enables read-only analytical access to local CSV, Parquet, JSON files or a DuckDB database through MCP, with table discovery, column profiling, guarded SQL querying, and explain plans for safe use with Claude Desktop and Claude Code.

Enables DuckDB database interaction through MCP, supporting SQL queries, table creation, and schema inspection with optional read-only mode.

Enables AI assistants to query Apache Iceberg tables on S3 via AWS Glue Data Catalog using DuckDB as the embedded query engine, supporting columnar Arrow reads with no data movement.

Enables LLMs to interact with DuckDB databases through MCP tools for SQL queries, table management, data import/export, and schema inspection, with optional read-only mode for safety.

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

An MCP server for MotherDuck and local DuckDB.

Read-only SQL MCP server for quantitative finance data (DuckDB) with stock quotes, financials, and historical K-lines, enabling cross-database JOIN queries.

Lets LLMs rank, compare, and connect actors and movies from local IMDb non-commercial datasets stored in DuckDB, with Wikidata enrichment.

MCP server that lets you ask questions about your Garmin training history in plain language. It ingests FIT files into DuckDB locally and exposes typed tools for analyzing activities, streams, and summaries.

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.

An MCP server that lets an LLM query a DuckDB warehouse of EuroLeague and EuroCup basketball data in natural language, offering tools for player and team stats, shot charts, boxscores, and fantasy draft analysis without contacting upstream APIs.

Exports vulnerability and asset data from Rapid7 Command Platform via the Bulk Export API into a local DuckDB database, enabling AI assistants to run natural language queries and SQL analysis with sandboxed security.

Enables querying and updating Markdown frontmatter metadata using DuckDB SQL, with optional semantic search capabilities for finding similar documents based on content.

Answers natural-language questions against Postgres, Oracle, Snowflake, Databricks, DuckDB and SQLite via db_read(question) and get_schema(), each separately grantable. The model only proposes SQL — shape, access, dialect and query plan are checked deterministically before any rows are read, and the server refuses rather than returning an unverified answer.

Natural language to SQL engine with multi-connector support (PostgreSQL, MySQL, Snowflake, BigQuery, DuckDB), document QA, semantic caching, and self-hosted MCP server.

Analyzes Salesforce permission architecture (profiles, permission sets, groups) to support RBAC remediation. It snapshots the permission model into DuckDB and provides tools for diffs, sensitive access detection, and verified decomposition proposals.

Enables AI agents to perform professional-grade geoprocessing tasks such as buffers, overlays, reprojections, and terrain analysis with deterministic tools and verifiable provenance.