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Scored across 5 tools
Each tool has a clearly distinct purpose: get_report retrieves results, list_garak_probes lists attacks, list_models lists models by type, list_model_types lists types, and run_attack executes an attack. There is no overlap in functionality, making tool selection unambiguous.
All tool names follow a consistent verb_noun pattern (e.g., get_report, list_garak_probes, run_attack). The naming is uniform across all tools, with no deviations in style or convention.
With 5 tools, the set is well-scoped for a Garak MCP server focused on AI security testing. Each tool serves a specific role in the workflow, from setup (listing models/probes) to execution (run_attack) and review (get_report), without unnecessary redundancy.
The tool surface covers the core AI security testing workflow: listing resources (models, probes, types), running attacks, and retrieving reports. A minor gap exists in lacking tools for configuring attack parameters or managing attack history, but agents can still perform essential tasks effectively.