Anyquery

Overview

Anyquery is a versatile SQL query engine that turns virtually any data source into a relational database. By leveraging SQLite’s lightweight architecture, it exposes files, databases, and even application data (such as Apple Notes, Notion, Chrome history, or Todoist tasks) behind a unified SQL interface. Developers can therefore write familiar SELECT statements to explore, aggregate, or transform data that would otherwise require custom APIs or manual parsing.

For AI assistants, Anyquery solves the perennial problem of data accessibility. The server implements the Model Context Protocol (MCP), allowing LLMs that support it to register the query engine as a context provider. Once connected, an assistant can issue SQL commands directly through MCP messages, retrieve results, and use them to answer user queries or drive downstream logic. This tight integration eliminates the need for separate middleware, simplifies data pipelines, and ensures that AI-driven workflows can interact with structured information in real time.

Key capabilities include:

• Multi‑source querying – plug in file formats (CSV, JSON, Parquet), relational databases (PostgreSQL, MySQL, SQLite), and application APIs through a growing plugin ecosystem.
• LLM connectivity – expose the engine to ChatGPT, Claude, Cursor, TypingMind, and others via MCP or function‑calling endpoints.
• MySQL compatibility – run Anyquery as a MySQL server, enabling existing tools like TablePlus, Metabase, or custom applications to query any source without code changes.
• Extensibility – add new data connectors or LLM integrations by writing lightweight plugins, thanks to the open plugin architecture.

Typical use cases span data analytics, knowledge management, and automation. A product manager might query a Notion database and an internal PostgreSQL instance in one statement to generate dashboards. A chatbot could retrieve a user’s calendar events from Google Calendar and combine them with local log files to offer context‑aware suggestions. In research, scientists can merge experimental data stored in CSVs with metadata from a lab database, all through SQL.

Integrating Anyquery into AI workflows is straightforward: start the MCP server (e.g., ), provide the returned ID to the LLM client, and begin issuing SQL commands. The server handles query parsing, plugin dispatch, and result serialization automatically, allowing developers to focus on higher‑level logic rather than data plumbing. Its unique advantage lies in this seamless bridge between arbitrary data stores and conversational AI, empowering developers to build smarter, data‑driven assistants with minimal friction.