What can you do with this server?

This server lets you query databases and documents using plain-English questions, manage agents and connectors, and monitor system health — all as an MCP toolset. * Ask natural-language questions (query): Translates plain English into SQL or document search, returning a natural-language answer with generated SQL and/or citations. Supports PostgreSQL, Snowflake, BigQuery, Redshift, MySQL, SQL Server, and DuckDB. * List agents (list_agents): Discover all available database/document agents (YAML knowledge bases). * Inspect agent schema (get_agent_schema): View tables, columns, data types, and foreign keys for any agent before querying. * Semantic caching: Returns rapid responses to similar previously asked questions without hitting LLMs or databases. * Submit feedback (submit_feedback): Rate query results (👍/👎) and optionally supply corrected SQL to improve future accuracy. * Check system health (health): Verify connectivity to the LLM API, Qdrant vector store, embedding daemon, and configuration. * Invalidate cache (invalidate_cache): Clear cached query results for an agent after schema changes or data refreshes. * List connectors (list_connectors): View all registered database connectors (credentials redacted). * View query history (get_query_history): Retrieve recent queries and feedback ratings for an agent, newest first. * Get cache stats (get_cache_stats): Inspect cached entry count and the Qdrant collection backing the semantic cache.

Which integrations are available for this server?

Allows querying Amazon Redshift databases using natural language. Allows querying Confluence documents and retrieving answers with citations. Allows querying DuckDB databases using natural language. Allows querying MySQL databases using natural language. Allows querying Notion documents and retrieving answers with citations. Uses OpenAI's language models to process natural language queries and generate SQL. Allows querying PostgreSQL databases using natural language. Allows querying Snowflake databases using natural language.

NLQueries

Official

by nlqueries

Overview Schema Related Servers Score Discussions

Python

Remote

nlqueries-core

PyPI Python License: BSL 1.1

NLQueries Core turns plain-English questions into validated SQL, builds a self-updating YAML knowledge base from your schema and query history, and exposes everything as an MCP server your AI assistant can call directly. It also answers questions from your documents (PDF, Word, Excel, Notion, Confluence) and can blend both in a single hybrid answer.

Website & docs: nlqueries.com

Features

Capability	Description
Database connectors	PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, SQL Server / Azure SQL, DuckDB — plus a generic SQLAlchemy connector for any other SQLAlchemy-reachable database, driven by a connection URL
Document connectors	PDF, Word, Excel, Notion, Confluence — ask questions over ingested documents with citations
Query pipeline	Filter, cluster, and parameterize query history into reusable `QueryCapsule` templates
Knowledge base	Auto-generated YAML schema + capsule file, with coverage reporting via `kb-stats`
Multi-agent orchestration	Routes each question to a SQL agent, document agent, or both in parallel (hybrid)
Conversational follow-ups	Carries context across questions so a follow-up like "and by region?" resolves against the previous query — on by default in `nlqueries query`, reset with `--new-session`
Semantic cache	Returns previously-answered similar questions in under 50 ms, no LLM or DB round-trip
Embedding daemon	Keeps the embedding model resident in memory — ~10 ms per call instead of ~9 s
LLM client	Anthropic, OpenAI, or any LiteLLM-supported provider
MCP server	Query execution and schema/knowledge lookup exposed as MCP tools for Claude, Cursor, etc.
CLI	`nlqueries` (or the shorter `nlq` alias) — connect, build, query, and inspect from your terminal

See docs/architecture.md for how these pieces fit together.

Related MCP server: Legion Database MCP Server

Quickstart

Prerequisite: Python 3.11+.

Option A — Docker (recommended)

Pulls the published nlqueries/core image from Docker Hub — no clone required, just the compose file:

curl -O https://raw.githubusercontent.com/nlqueries/nlqueries/main/docker-compose.yml

Create a .env file next to it with at least one LLM key:

ANTHROPIC_API_KEY=sk-ant-...
# or OPENAI_API_KEY=sk-...

Then start the stack:

docker compose up

This pulls nlqueries/core:latest and starts it alongside Qdrant (:6333), with the MCP server on :8080. Run CLI commands against the running stack from a second terminal:

docker exec -it nlqueries-core nlqueries health

Option B — pip install

pip install nlqueries-core
export ANTHROPIC_API_KEY=sk-ant-...   # or OPENAI_API_KEY
nlqueries health

Optional extras for specific connectors:

pip install "nlqueries-core[mysql]"     # MySQL
pip install "nlqueries-core[redshift]"  # Amazon Redshift
pip install "nlqueries-core[mssql]"     # SQL Server / Azure SQL
pip install "nlqueries-core[duckdb]"    # DuckDB
pip install "nlqueries-core[docs]"      # PDF / Word / Excel ingestion
pip install "nlqueries-core[wiki]"      # Notion / Confluence sync

Option C — Clone and install from source

No Docker required — for contributing, or to run against unreleased changes:

git clone https://github.com/nlqueries/nlqueries.git
cd nlqueries
python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\Activate.ps1
pip install -e ".[dev]"
export ANTHROPIC_API_KEY=sk-ant-...   # or OPENAI_API_KEY
nlqueries health

See CONTRIBUTING.md for linting and test commands.

First query

nlqueries connect postgres --host localhost --database mydb --user alice --password secret --alias dev
nlqueries process-history dev --days 30 --annotate
nlqueries export-kb dev
nlqueries query dev "How many orders shipped last month?"
nlqueries query dev "and how many were returned?"   # follow-up — keeps prior context

Follow-up context is on by default; pass --new-session to start fresh or --no-session to disable it for a one-off question.

Full walkthrough: docs/getting-started.md.

Documentation

Doc	Covers
docs/getting-started.md	Step-by-step setup and your first query
docs/cli-reference.md	Every command and flag
docs/connectors.md	Database and document connector setup, per-connector notes and caveats
docs/configuration.md	Environment variables
docs/troubleshooting.md	Common warnings and errors explained
docs/qdrant-setup.md	Setting up Qdrant (required for embeddings, semantic cache, document search)
docs/architecture.md	Module layout and request flow

Contributing

See CONTRIBUTING.md. All contributors must sign the CLA before a PR can be merged — see CONTRIBUTOR_LICENSE_AGREEMENT.md.

License

Business Source License 1.1 — each release converts to Apache 2.0 four years after its release date.

Install Server

license - not found

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

6dRelease cycle

2Releases (12mo)

Commit activity

Resources

GitHub Repository

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