ORKL MCP Server

Overview

The ORKL MCP Server bridges the gap between advanced threat‑intelligence data and AI assistants by exposing the ORKL Threat Intelligence Library through a Model Control Protocol interface. It solves the problem of fragmented, rate‑limited API access to security data by providing a local, cache‑enabled gateway that can be queried directly from LLMs such as Claude. Developers and security analysts no longer need to write custom API wrappers or manage OAuth tokens; the server handles authentication, request throttling, and persistence behind a simple set of tools and resources.

At its core, the server runs an HTTP service that implements MCP endpoints for fetching threat reports, actor profiles, and source metadata. It validates requests against the ORKL API rate limits (90 calls per 30 seconds by default) and caches responses for five minutes to reduce latency and avoid unnecessary traffic. The built‑in cache can be flushed on demand, giving analysts fine control over data freshness during investigations. Because the server conforms to MCP standards, it can be registered with Claude Desktop or any other MCP‑aware client using a single configuration block.

Key features include:

• Comprehensive toolset: From to , the server offers a full suite of operations that mirror the ORKL API, allowing LLMs to retrieve and manipulate data without leaving the conversation.
• Direct resource URLs: Resources such as provide immediate, typed access to specific entities, enabling precise data retrieval in prompts.
• Robust caching and rate limiting: These mechanisms protect the ORKL API from overuse while ensuring that responses are served quickly, which is critical for real‑time threat analysis.
• Zero‑config integration with Claude Desktop: A single JSON snippet launches the server and registers it as a tool, making setup trivial for users of the desktop client.

In practice, security teams can embed this server into their incident‑response workflows. For example, an analyst might ask a model to “check if hash X is known in ORKL and give attribution details,” triggering and returning structured JSON that the model can format into a report. Similarly, threat‑landscape overviews for specific sectors (financial institutions, healthcare) can be generated by searching recent reports and summarizing emerging trends—all within a single chat session.

By encapsulating ORKL’s rich threat intelligence behind MCP, the server offers developers a plug‑and‑play component that enhances AI assistants with real‑world security data, reduces development overhead, and ensures compliance with API usage policies.