ORKL MCP Server

@heiths

About ORKL MCP Server

MCP server for ORKL Threat Intelligence Library

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "orkl_mcp_server": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is ORKL MCP Server?

The ORKL MCP Server is an MCP (Model Control Protocol) server that provides access to the ORKL Threat Intelligence Library via the ORKL API. It is designed for developers and security analysts who want to query threat intelligence data from within LLM applications like Claude Desktop.

How to use ORKL MCP Server?

Install prerequisites (Python 3.10+ and the uv package manager), clone the repository, install dependencies with uv, and configure via environment variables (e.g., ORKL_API_BASE_URL) or a config.json file. Then register the server with Claude Desktop by adding a configuration entry to claude_desktop_config.json that points to the cloned directory and runs run_server.py using uv.

Key features of ORKL MCP Server

Access to ORKL Threat Intelligence Library via the official API
Efficient caching to minimize API calls
Rate limiting to respect ORKL API restrictions
Standardized MCP Tools and Resources for LLM interaction
Built-in support for Claude Desktop integration

Use cases of ORKL MCP Server

Malware investigation: Check a file’s SHA1 hash against known threats in the ORKL database
Threat landscape overview: Search for recent reports relevant to a specific sector (e.g., financial institutions) and identify emerging trends
Fetch and explore recent threat reports, threat actors, or sources
Retrieve detailed information about a specific report, actor, or source via direct resource URIs
Clear local cache to ensure fresh data on subsequent queries

FAQ from ORKL MCP Server

What is the ORKL Threat Intelligence Library?

ORKL is a threat intelligence library that provides structured data on malware, threat actors, and sources. The ORKL MCP Server accesses this data through the public ORKL API.

What are the prerequisites to run ORKL MCP Server?

You need Python 3.10 or later and the uv package manager (recommended). The server also requires a working internet connection to reach the ORKL API.

How do I configure the base URL or other settings?

Configuration can be provided via environment variables (e.g., ORKL_API_BASE_URL, ORKL_CACHE_TTL, ORKL_RATE_LIMIT_REQUESTS) or a JSON configuration file (config.json in the current directory). Default values are documented in the README.

Is caching enabled by default?

Yes, caching is enabled by default (ORKL_USE_CACHE is set to 1). The cache time-to-live is 300 seconds by default, but can be adjusted via the ORKL_CACHE_TTL environment variable.

How can I clear the cache?

The server provides an MCP tool clear_cache that can be invoked to clear the local cache and force fresh data on subsequent API requests.

Frequently asked questions

What is the ORKL Threat Intelligence Library?

ORKL is a threat intelligence library that provides structured data on malware, threat actors, and sources. The ORKL MCP Server accesses this data through the public ORKL API.

What are the prerequisites to run ORKL MCP Server?

You need Python 3.10 or later and the `uv` package manager (recommended). The server also requires a working internet connection to reach the ORKL API.

How do I configure the base URL or other settings?

Configuration can be provided via environment variables (e.g., `ORKL_API_BASE_URL`, `ORKL_CACHE_TTL`, `ORKL_RATE_LIMIT_REQUESTS`) or a JSON configuration file (`config.json` in the current directory). Default values are documented in the README.

Is caching enabled by default?

Yes, caching is enabled by default (`ORKL_USE_CACHE` is set to `1`). The cache time-to-live is 300 seconds by default, but can be adjusted via the `ORKL_CACHE_TTL` environment variable.

How can I clear the cache?

The server provides an MCP tool `clear_cache` that can be invoked to clear the local cache and force fresh data on subsequent API requests.

Comments

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