MCP server for attackmate

**Context**
implement an MCP server so that LLMs can interact with AttackMate

**Alternatives**
1) MCP server wraps the REST API (thin client)
The MCP server is a thin layer that translates LLM tool calls into HTTP requests to attackmate-api-server. AttackMate never runs in-process.

2) MCP server imports AttackMate directly (same pattern as api-server)
The MCP server instantiates AttackMate, calls run_command() / main() directly, same as commands.py and playbooks.py do now.

-> Choosing option 1 for less code duplication.

**why not use "just" fast api and llms interact with that?**
The REST API exposes AttackMate over the network but is not directly usable by LLMs, has no structured tool definitions.
The MCP server will act as a **thin translation layer**: it wraps the REST API, and exposes each operation as a typed _**tool**_ (with schemas auto-generated from the Pydantic models), and serves the existing RST **_resources._**
--> makes AttackMate directly drivable from MCP-compatible clients like Cursor or Claude Desktop **without any changes to the API server itself.**
TLDR:
fastAPI spec: describes only what endpoinds exist
whereas The MCP tools and docstrings and resource URIs embed behavioural guidance for an LLM