Db Query MCP

db‑query‑mcp is a Model Context Protocol server that bridges AI assistants with relational, document, and search‑engine databases. It addresses the common pain point of giving an AI agent direct, secure access to a variety of data stores without exposing raw connection strings or risking accidental writes. By running as an MCP server, the tool becomes a first‑class resource that any Claude or other AI client can discover and invoke through standard MCP calls.

At its core, the server accepts a natural‑language query, automatically translates it into an optimized SQL or ElasticSearch DSL statement, executes the query in a read‑only mode by default, and returns structured results. The ability to translate conversational prompts into precise database queries empowers developers to build chat‑based interfaces that can pull real‑time analytics, audit logs, or inventory data without writing boilerplate code. The read‑only safety net protects production databases from accidental mutations while still enabling powerful exploratory analysis.

Key capabilities include:

• Multi‑DB support: Native drivers for MySQL, PostgreSQL, SQLite, Oracle, SQL Server, and ElasticSearch are bundled. The server can be extended to MongoDB or graph databases through optional dependencies.
• Natural‑language to query conversion: The tool parses user intent and constructs efficient queries, handling common pitfalls such as ambiguous column names or unsupported functions.
• Result export: Returned data can be serialized to CSV or JSON, making it ready for downstream reporting tools or spreadsheet consumption.
• Secure configuration: Connection strings are supplied once at startup; the server never exposes credentials, and all operations default to read‑only unless explicitly overridden.

Typical use cases include:

• Data‑driven chatbot assistants that can answer questions about sales figures, user behavior, or system logs directly from the source database.
• Rapid prototyping of analytics dashboards where developers can test SQL logic in a conversational loop before committing to code.
• Audit and compliance tools that need read‑only snapshots of database state for reporting or forensic review.
• Integration with low‑code platforms that expose MCP resources, allowing non‑technical users to query complex datasets through a simple chat interface.

In an AI workflow, the MCP server is registered in the client’s resource catalog. When a user asks a question that requires data, the assistant can automatically invoke , pass the natural language prompt, and receive a structured response. Because the server handles all database specifics internally, developers can focus on higher‑level business logic while relying on a robust, secure data access layer that scales across heterogeneous environments.