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

Annotations already declare readOnlyHint=true, so the description's main behavioral contribution is explaining that the tool operates only on the authenticated user's own data. It adds the detail about subscription plans and next refresh date, providing modest context beyond what annotations alone convey.

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 well-structured with a primary paragraph and a clear scope section. It is concise without being under-specified, though the scope bullet could be slightly tightened.

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 simplicity (no parameters, read-only, no output schema), the description is nearly complete. It covers purpose, scope, and return value hints. The only minor gap is the lack of explicit success/failure response details, but that is acceptable for a read-only 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?

With zero parameters and 100% schema coverage, the description faces no burden. It adds value by explaining what the returned data includes (credits remaining, next monthly refresh date), giving the agent useful semantic context about the output.

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 specifies the verb 'Get' and the resource 'current Giggal.ai credit balance for the authenticated user'. It distinguishes itself from siblings by focusing solely on credits, not emails or verification details.

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 the scope (only authenticated user's own balance) and what it does not return (other users, verification methods, etc.). While it provides clear context and exclusions, it does not explicitly mention when to use this tool versus siblings.

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 mark the tool as readOnlyHint=true, which is consistent. The description adds significant behavioral context beyond the annotation: it states that results are scoped to the authenticated user's own history, that it never reveals verification methods or backend internals, and that it includes a fallback message. This fully informs the agent about what the tool does and does not expose.

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 four sentences long, well-structured, and free of unnecessary words. The first sentence states the purpose, the second gives usage context, the third describes fallback behavior, and a separate paragraph clearly defines scope. Every sentence 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?

Given the tool has one parameter, no output schema, and annotations present, the description covers purpose, usage, scope, privacy, and fallback behavior. It does not explicitly describe the return format or structure of the 'detailed info', which is a minor gap. However, overall it provides sufficient context for an agent to decide when and how to use 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 single parameter 'email' has a clear schema description, and schema coverage is 100%. The tool description does not add any additional semantics or formatting guidance beyond what the schema already provides. Baseline score of 3 is appropriate when the schema fully covers the parameter.

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 starts with 'Look up detailed info about a past verification of a specific email address', which is a specific verb-resource combination. It clearly distinguishes from the sibling 'verify_emails' by focusing on history lookup, not verification execution. The mention of a fallback message when no prior verification exists further clarifies the tool's unique role.

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 the tool: 'Useful when a user asks about a previous verification result.' It also provides context on what happens if no prior verification exists (suggests running verify_emails). However, it does not explicitly exclude use cases or name alternative tools beyond that single note, leaving some implicit 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?

Annotations provide readOnlyHint: true, which is consistent with verification. The description adds significant behavioral context beyond annotations: credit cost (1.5 per email), output fields, catch-all domain implications, and explicit scope boundaries (no disclosure of verification methods, infrastructure, etc.). No contradiction.

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 moderately long with multiple paragraphs, but each section serves a distinct purpose: core function, critical usage directive, presentation guidance, and scope boundary. It is front-loaded with the most important information and uses headings (ALWAYS, SCOPE) for scanability. Could be slightly more concise, but structure is logical and effective.

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?

Despite having an output schema (unseen), the description explicitly lists return fields and adds critical operational context (credit cost, batch processing expectation, catch-all behavior, scope limitations). With sibling tools covering different domains, this description fully prepares an agent for correct invocation and result handling.

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% with the 'emails' parameter already described as 'Email addresses to verify.' The description adds the maxItems=1000 and minItems=1 (already in schema) plus the usage instruction to pass all at once, which is non-parametric guidance. Since baseline is 3 for high schema coverage, the description adds marginal value but does not significantly enhance parameter understanding beyond what's in the schema.

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 ('Verify') and resource ('email addresses') with a specific scope ('up to 1000 per call') and lists exact return values (existence, disposable, catch-all, free provider, deliverability score). It distinguishes itself from siblings by focusing solely on verification, not details (get_verification_details) or credits (get_credit_balance).

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?

Provides explicit guidance: 'ALWAYS pass every email the user asked about in a SINGLE call' with reasoning about credit waste and fragmented results. Also specifies when to decline and redirect (scope paragraph), and instructs the agent to surface the 'catch_all_domain' field in results. This fully covers when and how to use the tool versus alternatives.

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