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Queryweaver MCP Server

@FalkorDB

Repository

Basic information

Category

Databases

Transports

stdio

Author

FalkorDB

Submitted by

Guy Korland

About Queryweaver MCP Server

An open-source Text2SQL tool that transforms natural language into SQL using graph-powered schema understanding. Ask your database questions in plain English, QueryWeaver handles the weaving.

#text2sql#semantic-layer#falkordb

Queryweaver MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "queryweaver": {
      "type": "http",
      "url": "https://app.queryweaver.ai/mcp",
      "headers": {
        "Authorization": "Bearer your_token_here"
      }
    }
  },
  "inputs": []
}

Queryweaver MCP Server tools

4

List all available graphs/databases for the authenticated user. Requires authentication.

Return all nodes and edges for the specified database schema. Requires authentication. args: graph_id (str): The ID of the graph to query (the database name).

Query the Database with the given graph_id and chat_data. Requires authentication. Args: graph_id (str): The ID of the graph to query. chat_data (ChatRequest): The chat data containing user queries and context.

Accepts a JSON payload with a database URL and attempts to connect. Supports both PostgreSQL and MySQL databases. Streams progress steps as a sequence of JSON messages separated by a delimiter. Requires authentication.

Overview

What is QueryWeaver?

QueryWeaver is an open-source Text2SQL tool that converts plain-English questions into SQL using graph-powered schema understanding. It helps you ask databases natural-language questions and returns SQL and results.

How to use QueryWeaver?

You can run QueryWeaver via Docker with a single command (docker run -p 5000:5000 -it falkordb/queryweaver) or from source using Python 3.12+ and pipenv. Configuration is provided through environment variables (e.g., Azure OpenAI or OpenAI API keys). QueryWeaver exposes a REST API with endpoints for managing graphs and running Text2SQL queries, and optionally provides MCP endpoints for integration with AI assistants.

Key features of QueryWeaver

  • Graph-powered schema understanding for Text2SQL
  • Plain-English to SQL conversion
  • REST API with Swagger UI documentation
  • Optional MCP server endpoints
  • Docker deployment (single command)
  • Supports Azure OpenAI and OpenAI
  • OAuth authentication (Google, GitHub)
  • Streaming responses with reasoning steps

Use cases of QueryWeaver

  • Natural language database querying without writing SQL
  • Automating SQL generation for dashboards and reports
  • Integrating database access into AI assistants and chatbots
  • Prototyping and data exploration for analysts and developers
  • Enabling non-technical team members to query databases

Frequently asked questions

1

What AI providers does QueryWeaver support?

QueryWeaver supports Azure OpenAI by default. To use OpenAI directly, set the `OPENAI_API_KEY` environment variable instead of `AZURE_API_KEY`.

2

How can I authenticate with the REST API?

All authenticated endpoints require a Bearer token in the `Authorization` header. In the browser the app uses session cookies and OAuth flows (Google, GitHub); for scripts you can use an API token.

3

Does QueryWeaver support the Model Context Protocol (MCP)?

Yes, QueryWeaver includes built-in MCP endpoints (`list_databases`, `connect_database`, `database_schema`, `query_database`). These are enabled by default and can be disabled by setting `DISABLE_MCP=true`.

4

What are the system requirements for running from source?

You need Python 3.12+, pipenv, a FalkorDB instance, and Node.js with npm (for the TypeScript frontend).

5

How does the streaming response work?

The `POST /graphs/{graph_id}` endpoint streams JSON objects delimited by the boundary string `|||FALKORDB_MESSAGE_BOUNDARY|||` containing intermediate reasoning steps, follow-up questions, and the final SQL.

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