Lists canary-based red-team probes for testing an LLM or chatbot, grouped by category; all probes are safe and avoid requesting harmful content.
List all red-team probes this tool can run against a target LLM/chatbot, grouped by category. Every probe is canary-based (safe) — none ask the target to produce genuinely harmful content.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.5Input schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"v0.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does meaningful work: it discloses that every probe is canary-based and safe, and that none request genuinely harmful content — exactly the concern an agent or operator would have about a red-team tool. It stops short of saying whether the call contacts a target at all or is purely a static catalog read.
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?
Two sentences, each earning its place: the first defines the operation and result shape, the second preempts the safety objection. Nothing is padded or restated from the name.
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 zero-parameter catalog tool with no output schema, the description covers what is returned (probes grouped by category) and the safety profile. It could go one step further and note whether the returned probe identifiers are the inputs expected by run_probe_suite, but nothing essential is missing for correct invocation.
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 tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly implies no inputs are needed.
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?
States a specific verb ("List") and resource ("red-team probes") plus the grouping structure of the result, which is enough to distinguish it from the sibling run_probe_suite. The phrase "probes this tool can run" is slightly imprecise, since list_probes itself doesn't run anything, but the intent is unambiguous.
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?
Listing probes implies a discovery step before running them, and the sibling run_probe_suite makes that workflow inferable, but the description never says 'use this to discover probes before calling run_probe_suite' or states any prerequisite or exclusion. Usage is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.