verification

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

With no annotations provided, the description carries the full burden. It discloses the cost ($1.00 per call, paid in USDC over x402), the type of checks performed, and the separation between holder-harm threats and issuer controls. This is strong transparency, though it does not explicitly state whether the operation is read-only or has side effects beyond payment.

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 three well-structured sentences: a purpose statement, an enumeration of detected threats and disclosed controls, and a cost note. It is front-loaded with the main purpose, contains no redundancy, and every sentence provides distinct, necessary information.

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 has no output schema, yet the description effectively communicates what the output will address (threats and issuer controls) and the cost model. It gives a high-level framework but lacks a bit of detail on the exact response format or additional limitations, which is slightly limiting for a security verification tool.

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 already provides detailed descriptions for all three parameters (chain, contract_address, subscription_token) with 100% coverage. The main description adds no additional parameter-specific semantics, so it does not go beyond the schema; the baseline of 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 begins with a specific verb phrase 'Issuer control & trust profile' and clearly identifies the resource as 'Base B20 native tokens.' It lists concrete threat categories (honeypots, armed freeze-and-seize, live pause, fake B20s) and control disclosures, which distinguishes it from sibling tools like ethereum_token_verification or solidity_code_verification.

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 chain parameter explicitly notes 'base for mainnet (default), or base-sepolia to check a token before you launch it,' which provides a clear when-to-use context for testing vs. production. However, it does not explicitly exclude other chains or name alternatives like ethereum_token_verification for Ethereum tokens, so it stops short of full usage guidance.

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 carries the burden of behavioral disclosure. It adds valuable context by stating the cost ($1.00 per call, paid in USDC over x402) and the dependency on @centry_agent, which indicates an external service. However, it does not disclose whether the tool is read-only, what the response format looks like, or any potential rate limits, leaving gaps in transparency.

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 concise, with two sentences covering purpose and cost. The key function is front-loaded in the first sentence. The mention of '@centry_agent' and cost details are relevant but could be considered slightly extraneous; overall, it earns its place without unnecessary 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?

The tool is simple with two parameters and no output schema, so the description is mostly adequate. It covers purpose and cost, but lacks explicit usage guidance compared to sibling tools (e.g., when to choose this over verification tools) and does not describe the output contents. Given the advisory nature, a bit more context would help, but it is minimally complete.

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 description coverage is 100%, so the baseline is 3. The description provides no additional information about the query or subscription_token parameters beyond what the schema already specifies. The schema descriptions are sufficiently clear, so no further compensation is 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?

The description clearly states the tool's function: providing expert advisory and real-time threat intelligence, analyzing threats, best practices, and compliance. The verb 'analyses' and resource 'threats, best practices & compliance' make the purpose specific. It distinguishes from sibling verification tools which focus on verifying specific items rather than offering general advisory.

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 for cybersecurity advisory and threat intelligence but does not explicitly state when to use it over alternatives. No exclusion criteria or mention of sibling tools like recent_exploits, leaving the decision partly to the agent. It provides a general context but lacks clear when-to-use or when-not-to-use guidance.

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?

No annotations are provided, so the description carries the transparency burden. It discloses the cost ($1.00 per call, paid in USDC over x402) and mentions AI-powered analysis, but does not indicate whether the tool is read-only, what happens on failure, or whether it stores/transmits data. This is adequate but not thorough.

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 three sentences, each adding distinct value: purpose, detection specifics, and cost. It is front-loaded with the core action and has no 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?

Without an output schema or annotations, the description gives substantial context: purpose, risk categories, and pricing. It does not mention the return format or limitations, but for a paid verification API with well-documented parameters, it is reasonably complete.

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 already describes all four parameters with 100% coverage. The description adds no parameter-specific semantics, only context about token contracts generally. Baseline of 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 clearly identifies the tool as a token verification service for EVM contracts, explicitly listing detection capabilities (rug pulls, hidden taxes, liquidity legitimacy, holder risks). It differentiates from the sibling base_token_verification by focusing on EVM broadly and naming specific token risk categories.

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 establishes clear context: use for token due diligence on EVM contracts. However, it does not provide explicit exclusions or alternatives (e.g., when to use base_token_verification or solidity_code_verification instead), so it falls short of a 5.

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 of behavioral disclosure. It adds that the tool is 'Free' and that it lists 'every' service, but does not explicitly state read-only behavior. However, 'list' inherently suggests a non-mutating operation, reducing the risk. The description provides useful context beyond the bare name.

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, front-loaded sentence that includes the tool's purpose, scope, output details, and cost ('Free'). Every word earns its place, with no redundant or vague phrasing.

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 list tool with no output schema, the description is complete: it states what is listed (every service), the specific fields included (price, inputs, SLA), and a key contextual fact (Free). This sufficiently covers the expected behavior for an agent to select and invoke the tool.

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 input schema is empty. The description correctly includes no parameter information, and since there are no parameters to explain, the baseline of 4 applies.

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 function: listing all Cybercentry verification services, including price, inputs, and SLA. The verb 'list' and the resource 'every Cybercentry verification service' are specific, and it distinguishes itself from sibling tools that perform verifications rather than listing them.

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 using this tool to get an overview of available services and their details (price, inputs, SLA) before selecting a specific verification service. It does not explicitly state alternatives or exclusions, but the context of sibling tools makes the use case clear enough.

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 carries the full burden. It adds useful behavioral context: the $1.00 per-call cost and USDC/x402 payment requirement. It also implies a non-destructive read-only operation (verification). However, it does not disclose what happens with the input data, whether any external API is called, or the return format, leaving some transparency gaps.

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, focused sentence that front-loads the core purpose and adds the critical cost/payment detail. No word is wasted, and it is easy to parse.

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 description explains the main function and cost, but does not mention return values, output structure, or error scenarios. Since there is no output schema and no annotations, the description should carry more of that burden. The tool has 5 parameters and requires external payment, so more guidance (e.g., what the verification result looks like) would improve completeness.

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%, so baseline is 3. The description adds meaning by linking image_uri to the 'avatar/logo image check' and media_file_path to the media verification, and mentions 'metadata extraction' which corresponds to extract_metadata. This goes beyond the generic schema descriptions.

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 function: confirming authenticity and provenance of media using C2PA content credentials, with metadata extraction and an avatar/logo check. This specific verb+resource distinguishes it from sibling verification tools that target tokens, wallets, or code.

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 provides clear context for when to use the tool (verifying media files and associated avatar/logo images). However, it does not explicitly name alternatives or state when NOT to use it, unlike the high-calibration example that listed a sibling tool.

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?

No annotations are provided, so the description carries the full burden. It adds valuable information about cost ($1.00 per call, paid in USDC over x402), but it does not disclose the return format, rate limits, required permissions, or side effects. It gives a partial, but not comprehensive, behavioral picture.

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 two sentences, front-loaded with the core purpose, followed by the cost and payment method. Every sentence earns its place with no redundant or vague wording.

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?

With 6 parameters, no output schema, and no annotations, the description needs to explain how parameters relate and what the tool returns. It covers purpose and cost, and hints at outputs via 'Identifies...' but leaves the role of 'tool', 'skills', 'message', 'sessionId', and 'subscription_token' mostly unexplained. It is adequate for basic understanding but incomplete for a tool of this complexity.

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 description coverage is 100%, giving every parameter a description, but those descriptions are extremely generic ('Tool to validate', 'Config', 'Skills'). The description adds that the audit targets AI agent configurations, which implies the 'config' parameter refers to that, but it does not clarify the other parameters. This is baseline 3.

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 a specific verb ('automated security audits') and resource ('AI agent configurations'), and it distinctly identifies the tool as a security audit tool, differentiating it from sibling verification tools like web_application_verification or solidity_code_verification.

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 provides clear context for when to use this tool: when auditing AI agent configurations for missing authentication, prompt injection, or unsafe permissions. It does not explicitly state when not to use it or name alternatives, but the target use case is well specified.

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 only behavioral disclosure is the cost of $1.00 per call paid via USDC/x402. No mention of permissions, side effects, failure modes, rate limits, or data handling. With no annotations provided, the description fails to convey important operational behavior.

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 brief and front-loaded, but the second sentence ('Enterprise-grade ZKP security') is a marketing claim that adds little actionable value. Overall, it is concise and easy to scan.

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 involving ZKP generation, the description does not explain what the tool returns, how proofs are delivered, or the precise meaning of 'text integrity.' With no output schema and no annotations, this leaves a significant gap for an AI agent deciding whether to invoke the tool.

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 description coverage is 100%, so the baseline is 3. The description adds no specific meaning to the parameters; the schema's description of 'cpdv_data' as 'Data (e.g., Test Data)' is vague, and the tool description does not clarify expected format or how it relates to ZKP generation.

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 function with a specific verb ('Validates') and resource ('text integrity') plus the outcome ('generates Zero-Knowledge Proofs'). This distinguishes it from many sibling verification tools, though no explicit comparison is made.

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?

No guidance is provided on when to use this tool versus sibling verification tools, nor any exclusions or prerequisites. The description only states what the tool does, leaving usage context implied.

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 cost and payment method (USDC over x402), which is useful behavioral context. However, it does not explain the verification aspect, data handling, or any side effects, leaving major behavioral traits unclear.

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 short, but the phrase 'Enterprise-grade quantum-safe encryption' is largely redundant with the first sentence's 'quantum-resistant encryption'. The cost line is useful, but the marketing language dilutes conciseness.

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 is a paid service with a required parameter, but the description omits what 'verification' entails, the return format, and how it fits with sibling verification tools. The mismatch between the name (verification) and description (encryption) creates a significant completeness 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?

Schema description coverage is 100% for both parameters, so the baseline is 3. The description adds no additional parameter meaning beyond what the schema already provides (e.g., cqcv_data is 'Any text data you want to encrypt and verify').

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 says 'Secures text data with quantum-resistant encryption', which is an encryption action, but the tool name is 'quantum_cryptography_verification' implying verification, not encryption. The purpose is vague and does not distinguish from sibling verification tools.

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?

No guidance is provided on when to use this tool versus alternatives. The cost mention is informative but not a usage guideline. There is no context about scenarios where this should be preferred.

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 content (loss amounts, attack vectors, related service) and notes it is free. It does not mention authentication, rate limits, or data format, but for a read-only feed this is acceptable.

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?

A single, well-structured sentence that front-loads the core purpose and includes useful details without waste. Every word 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?

For a simple tool with one optional parameter and no output schema, the description explains what users get (incidents with amounts, vectors, and relevant service). It lacks response format details, but for the tool's simplicity, it is sufficiently complete.

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 description coverage is 100% with the 'limit' parameter clearly described. The tool description does not add any additional parameter semantics, so the baseline score of 3 applies.

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 returns recent real-world web3 exploits with specific attributes (loss amounts, attack vectors, Cybercentry service). Although it lacks an explicit verb like 'list' or 'retrieve', the intended action is unambiguous and distinct from sibling verification tools.

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?

No explicit guidance on when to use this tool versus alternatives, but the unique focus on live exploits implies it's for up-to-date threat intelligence. No exclusions or alternative tool references are mentioned, making the usage context only implied.

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 carries the full burden. It discloses cost ($1.00 per call, USDC/x402), speed ('fast'), and output type ('risk-level assessment'), which are useful behavioral details. However, it does not explicitly state whether the operation is read-only or if there are any side effects (e.g., data transmission or storage), leaving some ambiguity.

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 two sentences long, with no wasted words. The first sentence conveys the core function and outcome, and the second provides critical pricing information. Both sentences earn their 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?

For a relatively simple tool with two parameters and no output schema, the description adequately covers purpose, cost, and implied return value (risk-level assessment). It could mention potential limitations or prerequisites, but these are not essential given the tool's straightforward nature.

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 description coverage is 100%, so both parameters are already documented. The description adds no additional parameter-specific meaning beyond what the schema provides, such as how to format the Solidity code or the purpose of the subscription token.

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 performs 'analysis of Solidity code' with a 'clear risk-level assessment', specifying both the resource and the outcome. It distinguishes itself from sibling tools by targeting Solidity code specifically, as opposed to token or media verification.

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 its use case: analyzing Solidity code for risk assessment. While it doesn't explicitly mention alternatives or exclusions, the domain is well-defined and distinct from sibling tools, giving clear context. It lacks an explicit 'when not to use' but is otherwise clear.

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 adds useful details like real-time analysis, chain coverage, cost, and payment method, but does not disclose output format, read-only nature, rate limits, or failure behavior, leaving significant uncertainty for an agent.

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 concise sentences convey the core functionality and cost without extraneous words. However, the first sentence is a noun phrase lacking a strong verb, which slightly reduces structural clarity.

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?

Without an output schema, the description should explain the return value, but it doesn't. It also omits use-case guidance, limitations, and error conditions, leaving the agent under-informed for a paid tool.

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 description coverage is 100%, so the schema already documents both parameters. The description adds no additional parameter-level detail beyond noting the cost, which indirectly relates to subscription_token but does not explain how to use it.

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 states the tool performs real-time blockchain security analysis and sanctions screening across 35 EVM chains, making its primary purpose reasonably clear from the name and context. However, it lacks an explicit verb+resource statement like 'verify a wallet address,' and does not distinguish itself from sibling token-verification tools beyond the wallet focus.

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?

No guidance on when to choose this tool over sibling tools (e.g., ethereum_token_verification for tokens). The description mentions cost and payment but provides no context about suitable use cases or prerequisites.

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 must disclose behaviors. It does mention the per-call cost and payment method (USDC via x402), which is helpful. However, it omits crucial details like whether the tool actively scans/sends requests, expected output format, or any prerequisites. This leaves significant behavioral unknowns.

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 with zero waste: front-loaded purpose followed by cost. Efficient and readable.

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 description covers what and cost, but lacks an output schema it must compensate for the absence of return value details. For a complex security tool, behavioral expectations (e.g., whether it's safe to run on production) are missing, making this minimally viable but not fully complete.

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 description coverage is 100%, so the baseline is 3. The description itself adds no parameter-specific info beyond what's in the schema, but the schema's own descriptions (e.g., subscription_token) are already clear, so no penalty is warranted.

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?

Description clearly specifies the tool's function: securing websites, dApp frontends, and web interfaces via OWASP-powered vulnerability detection. This distinctively separates it from sibling tools like ethereum_token_verification or media_content_verification, which focus on different resource types.

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's domain specificity (web applications) makes usage context clear, but it stops short of explicitly naming alternatives or exclusions—e.g., it doesn't say 'use solidity_code_verification for smart contracts instead.'

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