Huntress API MCP Server

Overview

The Huntress API MCP Server bridges the gap between an AI assistant and the full suite of Huntress security operations. By exposing a curated set of tools—account queries, organization and agent management, incident and summary reports, and billing data—the server lets developers programmatically orchestrate security workflows without leaving the AI environment. This eliminates the need for separate SDKs or manual API calls, allowing a single prompt to retrieve incident details, list active agents, or generate billing summaries.

What problem does it solve? Many security teams rely on Huntress for continuous monitoring, incident response, and cost tracking. However, accessing this data typically requires REST calls with authentication headers, pagination handling, and error parsing. The MCP server abstracts these complexities into declarative tool calls that the AI can invoke directly. Developers no longer need to write boilerplate code for authentication or rate‑limit handling; the server manages API keys, enforces a 60‑request‑per‑minute sliding window, and normalizes responses into consistent JSON structures.

Key capabilities are delivered through a simple, human‑readable interface. The server offers tools such as , , and . Each tool maps to a specific Huntress endpoint, returning structured data that the AI can ingest and summarize. Built‑in error handling captures common API failures—invalid credentials, exceeded rate limits, or malformed requests—and translates them into user‑friendly messages. This reliability is critical when the assistant must act autonomously in time‑sensitive security contexts.

Real‑world use cases abound. A DevOps engineer could ask the AI to “list all agents with a status of unhealthy” and receive an instant, formatted response. A security analyst might request a “summary report for the last 30 days” to surface trends without opening the Huntress dashboard. Billing teams can pull up the latest invoice details or generate cost projections, all from a single conversational interface. Because the server exposes both read‑only and management endpoints, it also supports automation scripts that spin up or retire agents based on policy changes.

Integration into AI workflows is straightforward. Once the MCP server is registered in an assistant’s configuration, each tool becomes a callable action within prompts. The AI can chain calls—first list incidents, then fetch details for the most recent one—mirroring how a human would navigate the Huntress UI. The server’s consistent output format ensures that downstream natural language generation or data visualization modules can consume the results without additional parsing logic.

Unique advantages of this MCP server include its lightweight Node.js implementation, strict adherence to Huntress’s rate limits, and comprehensive error handling that protects against common pitfalls. By centralizing all Huntress interactions behind a single, well‑documented MCP interface, developers gain a robust, secure bridge between their AI assistants and the Huntress platform—streamlining incident response, monitoring, and reporting into a unified conversational experience.