email-validator

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

No annotations are provided, so the description carries the full transparency burden. It discloses the checks performed but omits any behavioral traits such as whether the operation is read-only, network dependencies, potential delays, permission requirements, or output format details. Only the verb 'check' implicitly suggests non-mutation.

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 efficiently lists the checks without extraneous words. It earns its length by providing concrete detail about the tool's scope.

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 single-parameter tool, the description adequately conveys the tool's purpose and scope. However, with no output schema, it doesn't explain the return format, score ranges, or potential error conditions, leaving some ambiguity for an agent deciding whether this tool meets its needs.

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 covers 100% of the single parameter ('Domain to check (e.g. gmail.com)'). The description adds context about what checks the domain undergoes, which slightly enriches the parameter's meaning, but it doesn't provide additional format or validation details. 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 clearly states the action ('Check domain reputation') and specifies the exact resources checked (MX, SPF, DKIM, disposable detection, A record existence, reputation score). It distinguishes itself from sibling validate_email by focusing on domain-level checks, though it doesn't explicitly name the sibling.

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 when domain-level reputation verification is needed, listing specific checks. However, it provides no explicit when-to-use vs. alternatives like validate_email or suggest_fix, nor any exclusions or prerequisites. The implied context is clear but not fully developed.

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 tool's role and provides an example of the input/output transformation, but it does not state the exact return format (e.g., a list of suggestions, corrected email only, or behavior when no typo is found). This is a gap for agent invocation.

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 with an illustrative example. Every word adds value, and the structure front-loads the core purpose. No wasted text.

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 one parameter and no output schema, so the description should explain return values. It does not explicitly state what the tool returns or behavior for valid emails, leaving some ambiguity. The provider list and example help, but the missing output description prevents a higher score.

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 describes the 'email' parameter fully (100% coverage), and the description adds no extra semantic detail beyond the schema. Baseline 3 is appropriate since the schema handles parameter meaning.

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 ('Detect') and resource ('common email typos') with a concrete example ('user@gmial.com -> user@gmail.com'). It clearly distinguishes from sibling tools like validate_email or check_domain by focusing on suggestion, not just 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 for email typo correction and lists supported providers, giving clear context. However, it does not explicitly mention when not to use it or name alternative tools, so it falls short of full guideline coverage.

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 batch behavior, the maximum count, and the high-level return format. However, it does not mention error handling, side effects, or any constraints beyond the count limit, leaving room for more 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?

Two crisp sentences, front-loaded with the action and resource. Every word adds value without redundancy.

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 single-parameter tool with no output schema or annotations, the description covers the core behavior, constraints, and return summary. It lacks details on error representation or edge cases, but is reasonably complete given the tool's simplicity.

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 description adds no new insight beyond what the schema already provides for the 'emails' array. The description reinforces the max 100 but does not explain parameter meaning in more depth.

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 the specific verb 'validate' and resource 'email addresses', and adds 'Batch' to distinguish from sibling tools like validate_email. Mentions the limit of 100 and the output of individual results plus summary stats, making the 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?

Implies usage for validating multiple email addresses at once, but does not explicitly compare with sibling tools or state when not to use it. The phrase 'at once' gives some context, but no clear alternative/exclusion guidance is provided.

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 burden and lists a detailed set of checks (syntax, MX, disposable, role-based, SPF/DKIM, typo suggestions, risk score). This is transparent about what the tool does, but it omits details about the return structure, potential errors, and network dependencies.

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 sentence that front-loads the core action ('Validate a single email address') and efficiently lists all validation aspects without redundancy. Every phrase adds value.

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 a simple one-parameter tool with no output schema, the description provides a thorough overview of the validation logic and a risk score range. It could be more complete by stating what the return value looks like (e.g., a result object), but the listed checks and score range give enough context for typical 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?

The schema already fully describes the only parameter ('email') with a clear definition. The description adds no parameter-specific meaning beyond the validation checks, but since schema coverage is 100%, it does not need to compensate. 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 clearly states the tool validates a single email address and lists specific checks, distinguishing it from siblings like validate_batch (batch) and check_domain (domain-only). The verb 'validate' plus resource 'email' is specific and 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 makes clear it is for single email validation with comprehensive checks, implying it should be used when detailed validation is needed rather than simple domain checks or batch processing. However, it does not explicitly name alternative tools or state when not to use it, so it lacks strong exclusion guidance.

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