Axiom MCP Server

Axiom MCP Server Overview

The Axiom MCP server bridges Claude and other Model Context Protocol clients with the powerful analytics platform Axiom. By exposing a set of well‑defined tools, it lets AI assistants query and explore structured data stored in Axiom datasets without leaving the conversational interface. This removes a common friction point for developers who need real‑time data insights: instead of writing API calls or building custom dashboards, the assistant can directly execute Axiom Processing Language (APL) queries and retrieve metadata through simple prompts.

At its core, the server implements six distinct tools that map to common Axiom operations. sends an arbitrary APL query to the platform and streams back the result set, enabling ad‑hoc analysis or reporting. and provide introspection capabilities, allowing agents to enumerate available data sources and understand their column types. surfaces user‑starred or previously authored APL queries, fostering reuse and collaboration. Finally, and expose Axiom’s monitoring infrastructure, letting assistants alert on anomalies or retrieve historical execution logs. All tools are authenticated via an API token, ensuring secure access to the customer’s data.

For developers building AI‑powered analytics workflows, this server offers a concise and consistent interface. An assistant can ask, “Show me the top 10 error rates in the last 24 hours,” and the server translates that into an APL query, retrieves the result, and presents it in natural language. The same agent can then request a schema overview or list of available datasets, all within the same conversation. This tight integration eliminates context switching between IDEs, dashboards, and chat windows, accelerating prototyping and reducing cognitive load.

The Axiom MCP server shines in environments where data is rapidly changing and insights need to be delivered on demand. Security‑critical operations can benefit from the monitoring tools, enabling agents to surface alerts or historical trends without manual intervention. Data scientists can prototype exploratory queries through conversation, iterate quickly, and then lock in the final APL scripts for production use. Finally, developers building custom workflows can embed the MCP server in their own tooling stack, allowing any downstream application that supports MCP to tap into Axiom’s analytics engine seamlessly.

Unique advantages of this implementation include its lightweight binary distribution, comprehensive rate‑limiting configuration (query, dataset, and monitor limits), and full compatibility with the Claude desktop app. While it currently lacks support for resources or prompts, its focused toolset provides a solid foundation for building advanced AI‑driven data exploration experiences.