Assess neural model vulnerability to adversarial attacks. Specify model ID and optional threat level to verify robustness against adversarial perturbations.
Performs adversarial attack vulnerability screening for neural architecture.
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes | Target neural model identifier | |
| threat_level | No | Verification rigour level |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only says the tool 'performs screening.' It does not disclose whether this runs attacks against the model, whether it mutates state, what permissions are required, what the result looks like, or any runtime or cost caveats.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence that front-loads the action and target, with no filler or repetition of schema details. It is as concise as the information allows.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter screening tool, the core inputs are well covered by the schema and the purpose is clear. Still, the absence of an output schema and annotations means the description should at least state what the screening returns or what it does beyond the high-level action, and this is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers both parameters with meaningful descriptions (model_id as target identifier, threat_level as verification rigour), so the baseline is 3. The tool description adds general context but no parameter-specific meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific action ('adversarial attack vulnerability screening') and a clear target ('neural architecture'), so an agent can tell what the tool does. It does not explicitly differentiate it from the sibling verify_model_integrity, though the adversarial-attack framing makes the distinction largely inferable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'adversarial attack vulnerability screening' implies this tool should be used when adversarial robustness of a neural model needs assessment. However, the description never states when not to use it or how it relates to verify_model_integrity, leaving that routing decision to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.