DolphinDB MCP Server

Overview

The DolphinDB MCP Server bridges the gap between advanced time‑series databases and AI assistants that use the Model Context Protocol (MCP). By exposing a lightweight MCP interface, it allows conversational agents—such as Claude or other LLM‑powered tools—to query and manipulate DolphinDB data without requiring direct database access. This server solves the common problem of integrating high‑performance analytical databases into AI workflows while preserving security, scalability, and developer ergonomics.

What the Server Does

When launched, the server registers a set of MCP tools that wrap essential DolphinDB operations. Developers can call functions like , , , and from within their AI assistant. Each tool executes the corresponding DolphinDB command on a configured cluster and returns results in JSON‑friendly formats. The server automatically manages connection pooling, authentication, and error handling, so the assistant can focus on natural language understanding rather than database plumbing.

Key Features

• Unified MCP Interface – All database interactions are exposed through a single, well‑defined protocol that is compatible with any MCP‑compliant AI client.
• Secure Credential Management – Connection details are sourced from environment variables or a file, keeping secrets out of code and version control.
• Extensible Toolset – The design encourages adding new tools (e.g., data ingestion, table creation) without modifying the core server logic.
• FastMCP Compatibility – The server’s endpoints are ready for FastMCP agents, enabling instant integration with LLM toolchains.
• Zero‑Configuration Defaults – If environment variables are omitted, the server falls back to DolphinDB’s default host, port, and credentials, simplifying local development.

Use Cases

1. Financial Analytics – Analysts can ask an AI assistant to retrieve historical price tables, compute disk usage statistics, or run custom scripts on the fly.
2. Data Engineering – Engineers can automate database checks and maintenance tasks through conversational commands, reducing manual scripting.
3. Rapid Prototyping – Start‑ups can prototype data pipelines by asking an LLM to generate and execute DolphinDB queries without writing boilerplate code.
4. Compliance Auditing – Auditors can query database metadata and usage metrics via natural language, ensuring traceability and accountability.

Integration into AI Workflows

The server fits seamlessly into existing MCP pipelines: a FastMCP agent points to the local or cloud‑hosted instance, and the AI assistant invokes the exposed tools as part of its reasoning process. Because MCP standardizes request/response formats, developers can compose complex workflows—such as fetching data, applying statistical models, and returning visualizations—all within a single conversational turn. This tight coupling eliminates the need for separate REST APIs or custom SDKs, streamlining development cycles and reducing latency.

Unique Advantages

Unlike generic database connectors, the DolphinDB MCP Server is purpose‑built for time‑series analytics. It leverages DolphinDB’s columnar storage and in‑memory execution to deliver fast query responses, even for large financial datasets. Its minimal footprint (a single Python package) and declarative configuration make it ideal for micro‑service architectures, while the MCP standard ensures future‑proof integration with emerging AI platforms.