LLM Red-Team Scanner

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the informative nature and the payment options, implying this is a read-only lookup. It does not explicitly state that the call has no side effects, what the response format is, or whether 3 USDT is a displayed price or an actual charge.

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?

The description is extremely compact: a label followed by a dense, slash-separated list of concrete values. It front-loads the core purpose and every element—35 probes, 17 attack classes, 0-100 score, CI gate, 3 USDT, ERC-20, Lightning—carries meaningful information without filler.

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 no-argument pricing getter, the description provides enough content to let an agent answer the most likely pricing and payment questions. It could be more complete by noting whether 3 USDT is one-time or recurring and explicitly routing actual purchases to get_purchase_flow, but these are helpful additions rather than essential blockers.

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 input schema has zero parameters, so there is nothing for the description to document beyond what the schema already makes obvious. The pricing details are orthogonal to parameters整理, and the baseline for a zero-parameter tool is met.

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 clearly identifies the resource as 'PRO pricing & payment info' and enumerates the exact contents an agent can expect: probes, attack classes, score range, CI gate, and the 3 USDT price. It is unambiguous, though it lacks an explicit verb and does not contrast itself with get_purchase_flow, which also touches payment-related information.

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 use case is implied by the phrase 'PRO pricing & payment info': an agent would call this when the user asks about cost or payment methods. However, the description gives no explicit guidance about when not to use it or how it relates to get_purchase_flow, run_free_scan, or get_scan_result.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses key behavioral traits: it is a machine-readable procedure, involves payment of 3 USDT, and includes callback push with poll fallback. This goes beyond the empty schema and no annotations, providing critical context about the autonomous purchase flow. It doesn't mention potential risks like irreversible payment, but the core behavior is well described.

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?

The description is a single, dense sentence that packs essential information: machine-readable, autonomous, payment amount, delivery method, and no human in the loop. Every phrase earns its place, and the key details are front-loaded.

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 parameterless tool with no output schema, the description covers the essential context: what the procedure is, what it costs, and how delivery works. It doesn't describe the exact steps or return format, but given the tool's nature as a procedure reference, the description is reasonably complete. It could mention what the agent should do with the result, but this is a minor gap.

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 has zero parameters, so the schema provides no parameter information. The description compensates by explaining the procedure's purpose and mechanism, which is sufficient for a parameterless tool. Baseline 4 for zero params is appropriate.

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 clearly states the tool's purpose: it provides a machine-readable autonomous purchase procedure for paying 3 USDT and receiving the PRO download without human involvement. It distinguishes itself from siblings like get_pricing and run_free_scan by focusing on the purchase flow.

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 description implies when to use this tool: when an agent needs to autonomously purchase PRO access. It doesn't explicitly state when not to use it or name alternatives, but the context of siblings (get_pricing, get_scan_result, run_free_scan) makes the use case clear. It could be improved by explicitly contrasting with get_pricing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must fully disclose behavioral traits. It states the polling behavior and the done condition, but does not describe error handling, timeouts, whether it blocks, or the response structure. The agent is left without information about failure modes or what 'done' actually returns.

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?

The entire description is a single front-loaded sentence with no redundant words. It states the action, the input source, and the termination condition efficiently, earning its place with no fluff.

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 tool that presumably returns scan results, the description does not explain what the response contains, what statuses exist beyond 'done', or how failures are indicated. Since there is no output schema, this is a significant gap that could lead an agent to mishandle the result.

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 provides only the type (string) for job_id with zero description coverage. The description adds critical context that the job_id originates from run_free_scan, which tells the agent where to obtain it. This meaningfully compensates for the schema's lack of documentation.

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 clearly states the verb 'poll' and the resource 'a scan by job id', and it explicitly ties the job id to the output of run_free_scan, which distinguishes it from siblings. It also defines the termination condition ('until status=done'), making the tool's purpose 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?

The description implies the correct usage context by noting the job id is returned by run_free_scan, indicating this tool should be used after initiating a scan. It does not explicitly mention when not to use it or alternatives, but the flow is clear enough for an agent to select it appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the specific probes (jailbreak, prompt-injection, system-prompt-extraction, tool-abuse), the output (0-100 score, level, full report), and the approximate runtime (~35s). It doesn't mention side effects or asynchronous behavior, but the disclosed detail is substantial for a read-only scan.

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?

The description is a single, information-dense sentence with no fluff. It front-loads the most important fact ('Free LLM red-team scan'), then specifies probe types, output, and runtime. Every clause earns its place.

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?

The tool's behavior is well‑described for a single call, but the presence of get_scan_result suggests a possible async or step‑wise workflow that isn't addressed. The description doesn't clarify whether the report is returned directly or requires a follow‑up call, which could confuse agents. The ~35s runtime hints at a synchronous call but is ambiguous.

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?

Schema coverage is 100% and the parameter description ('Your agent / system prompt (>=20 chars)') is clear. The tool description adds context about the scan intent but doesn't provide new syntactic or format details for the parameter beyond the schema, so a baseline 3 is appropriate.

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 uses a specific verb ('run') with a clear resource ('free LLM red-team scan') and enumerates the adversarial probe types. It clearly distinguishes from the get_* siblings by stating it executes a scan rather than retrieving pricing, purchase flow, or results.

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 description implies usage ('scan your agent or system prompt') but does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. It doesn't clarify whether to call get_scan_result afterwards, which would be useful given the sibling set.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.