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

The description adds valuable context beyond annotations: it specifies that no payment is required (addressing cost implications) and clarifies the purpose as a verification tool. Annotations already cover read-only, non-destructive, and idempotent behavior, so the bar is lower, but the description enhances understanding without contradicting annotations.

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 front-loaded with the core purpose, followed by key usage notes, all in two concise sentences. Every sentence adds value: the first defines the tool's function, and the second provides practical guidance and exclusions, with no wasted words.

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?

Given the tool's low complexity (1 optional parameter, no output schema), rich annotations (covering safety and behavior), and high schema coverage, the description is mostly complete. It effectively communicates purpose and usage, though it could briefly note the idempotent nature or lack of output schema for full 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?

The description does not mention the 'verbose' parameter or its semantics. However, with 100% schema description coverage, the schema fully documents the parameter, so the baseline is 3. The description adds no additional parameter information, but it doesn't need to compensate for gaps.

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 specific action ('Check'), resource ('CryptoGuard API health, version, and service status'), and scope ('verify the service is running'), distinguishing it from sibling tools that perform different operations like checking tokens or scanning. It goes beyond a tautology by detailing what aspects of the API are checked.

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 explicitly states when to use this tool ('Use this to verify the service is running') and provides a key exclusion ('No payment required'), which helps differentiate it from potential paid alternatives or other tools that might require authentication. It offers clear guidance on its intended context.

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?

Annotations already cover key behavioral traits (read-only, open-world, idempotent, non-destructive), so the description adds value by detailing the six specific risk factors scored (liquidity depth, pair age, etc.) and providing an example. It does not contradict annotations, but it lacks additional context like rate limits or response format.

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 front-loaded with the core purpose, followed by specific risk factors and a practical example. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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?

Given the tool's complexity (assessing multiple risk factors) and lack of output schema, the description is mostly complete—it explains what the tool does and the factors considered. However, it does not detail the output format (e.g., how scores are returned), which could be helpful for an agent. Annotations provide good behavioral coverage, compensating partially.

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 clear descriptions for both parameters ('chain' and 'pair_address'). The description does not add meaning beyond the schema, as it focuses on the tool's purpose and risk factors rather than parameter details. Baseline 3 is appropriate given the high schema coverage.

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 specific action ('Assess rug pull risk') and resource ('for a specific DEX trading pair'), distinguishing it from siblings like health checks, token scans, searches, and trade validation. It provides concrete examples of the six risk factors being scored, making the purpose explicit and differentiated.

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 context through the example ('check if a new Solana pair is a potential rug pull'), suggesting it's for evaluating new or suspicious pairs. However, it does not explicitly state when to use this tool versus alternatives like 'cryptoguard_scan_token' or 'cryptoguard_validate_trade', nor does it provide exclusions 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?

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations by explaining the tier-matching peer comparison method and the types of outputs (anomaly scores, risk level, explanations), which helps the agent understand the tool's 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 front-loaded with the core purpose, followed by key details and a practical example. Every sentence adds value without redundancy, making it efficient and 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?

Given the tool's moderate complexity, rich annotations, and lack of output schema, the description is mostly complete. It explains the scanning method and output types but could benefit from more detail on result interpretation or error handling. However, it provides sufficient context for effective use.

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 fully documents both parameters. The description does not add any additional meaning or syntax details beyond what the schema provides, such as elaborating on the 'sensitivity' multiplier's impact. Baseline 3 is appropriate when the schema handles parameter 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 specific action ('Scan a single token for anomalous market behavior'), the method ('using WaveGuard physics-based anomaly detection'), and the comparison scope ('TIER-MATCHED peers'). It distinguishes this tool from siblings by focusing on anomaly detection rather than health checks, rug pulls, search, or trade validation.

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 by providing an example ('scan 'solana' to check if its metrics are unusual'), but it does not explicitly state when to use this tool versus alternatives like cryptoguard_rug_check or cryptoguard_validate_trade. No exclusions or prerequisites are mentioned.

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?

Annotations already cover key behavioral traits (read-only, open-world, idempotent, non-destructive), so the bar is lower. The description adds valuable context by specifying the search scope ('CoinGecko coin ID') and the types of inputs accepted ('name, symbol, or contract address'), which enhances understanding beyond the annotations. No contradictions with annotations are present.

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 front-loaded with the core purpose in the first sentence, followed by usage guidance and an example. Every sentence serves a clear function without redundancy, making it highly efficient and 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?

Given the tool's low complexity (single parameter, no output schema), rich annotations, and clear sibling relationships, the description is nearly complete. It effectively explains the tool's role in the workflow and input semantics. A minor gap is the lack of detail on output format (e.g., what the search returns), but annotations and context mitigate this.

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 parameter 'query' well-documented in the schema as 'Token name, symbol, or contract address to search.' The description reinforces this by listing the same input types but doesn't add significant new semantic details beyond what the schema provides, meeting the baseline for high coverage.

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 specific action ('Search for a token's CoinGecko coin ID') and the resources involved ('by name, symbol, or contract address'). It explicitly distinguishes from siblings by mentioning 'scan_token' and 'validate_trade' as tools that require the coin ID found through this search, establishing a clear workflow relationship.

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 explicit guidance on when to use this tool ('Use this first if you're unsure of the correct coin_id for scan_token or validate_trade') and includes a practical example ('search 'pepe''). It clearly positions this as a preparatory step for other tools, offering strong contextual direction.

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?

Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true. The description adds valuable behavioral context beyond annotations: it specifies the 5 specific checks performed (peer anomaly scan, self-history comparison, rug pull risk assessment, CEX/DEX price cross-check, concentration risk analysis) and the three possible verdict outcomes (PROCEED, CAUTION, BLOCK). This gives the agent important information about what the tool actually does.

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?

Perfectly structured and concise. First sentence states the core purpose, second sentence explains the return value, third sentence lists the 5 checks performed, fourth sentence provides parameter guidance, and final sentence gives a concrete example. Every sentence earns its place with zero waste.

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 with rich annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) and 100% schema coverage, the description provides excellent context about what the tool actually does, the checks performed, and the verdict system. The only minor gap is the lack of output schema, but the description adequately explains the return values (PROCEED, CAUTION, BLOCK verdicts).

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 fully documents all 5 parameters. The description adds some context by mentioning 'Accepts token name, symbol, or contract address' which reinforces the token parameter flexibility, and the example shows usage with amount_usd. However, it doesn't add significant meaning beyond what's already in the comprehensive 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 specific purpose: 'Validate a crypto trade BEFORE execution' with the verb 'validate' and resource 'crypto trade'. It distinguishes from siblings by focusing on pre-execution validation rather than health checks, rug checks, scans, or searches mentioned in sibling 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?

Explicitly states when to use: 'BEFORE execution' and provides a concrete example ('validate buying $500 of PEPE before executing'). It implicitly distinguishes from alternatives by listing the 5 specific checks performed, which helps differentiate from sibling tools like cryptoguard_rug_check that might only do one type of check.

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