How it works
The assistant calls three tools in sequence:1
opfor_list_evaluators
Discover available evaluator IDs and suites. Call this first when you haven’t named specific evaluators.
2
opfor_setup
Configure a target and write
opfor.config.json. Returns the config path.3
opfor_run
Generate attacks on the fly, run them, judge responses, and write HTML + JSON reports.
opfor_run handles generation, execution, and reporting in one shot.
Install
npx without cloning (see the npx tab above).
Register the server
- Cursor
- Claude Desktop
- npx (recommended)
Add to
~/.cursor/mcp.json (global) or .cursor/mcp.json (project-scoped):.env automatically — no env block needed in the config.
Usage
Once registered, just talk to your assistant:opfor_list_evaluators → opfor_setup → opfor_run and returns a findings summary in chat, with reports saved to disk.
Tools reference
opfor_list_evaluators
No parameters. Returns every evaluator ID, severity, standards map, and all predefined suites.
opfor_setup
Configures a run and writes opfor.config.json. Returns the config path to pass to opfor_run.
Target
Target
Agent target (
target_kind = "agent"):Client-owned targets set only
agent_session_send_*; server-owned targets (the target returns its own id) also set agent_session_receive_*. See session handling.MCP target (target_kind = "mcp"):Evaluators, LLM, and run settings
Evaluators, LLM, and run settings
Evaluator selection (pick one):
LLM configuration:
LLM object:
{ provider, model, api_key_env, base_url? }.Run settings:opfor_run
Runs the full evaluation from a config produced by opfor_setup.
API key resolution
The server resolves keys in order:- The env var named in the LLM config’s
api_key_env - The provider’s default env var (e.g.
OPENAI_API_KEYforopenai)
.env from the working directory automatically — keys never go in the MCP config JSON.
Providers
Supported providers and their default env vars.