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 states that the tool verifies MX records and returns mail server details, and adds the important trait that it does not count against verification quota. This covers the core behavior, though it could mention failure/success signaling more explicitly.

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 primary purpose, then the mechanism, followed by usage guidance and a quota note. Every sentence earns its place with no 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 simple one-parameter tool with no output schema, the description covers the purpose, usage, and key behavioral trait (no quota). It mentions 'returns mail server details' which gives a sense of the output, though a bit more detail on the return format would be beneficial.

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 covers the single parameter 'domain' with a type, description, and example. Schema description coverage is 100%, so the description adds no additional parameter semantics. 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 checks if a domain can receive email by verifying DNS MX records, and explicitly distinguishes it from email verification tools by noting it does not count against verification quota. This differentiates it from siblings like verify_email and find_emails.

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 says to use this tool 'to filter out dead domains before running find_emails', providing a concrete when-to-use scenario. It also implies a lightweight pre-screening role ('Quick check') and notes quota savings, but does not explicitly name alternative tools for other cases.

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 and performs well by disclosing specific behaviors: it scrapes websites, generates pattern-based candidates from a contact name, verifies via SMTP, and returns emails sorted by confidence score. This goes well beyond a simple 'find emails' statement, though it does not mention rate limits or failure modes.

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 tightly structured in three sentences: purpose, process, and unique value proposition. Each sentence contributes substantive information without redundancy, making it appropriately sized and 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?

Given the tool's moderate complexity (multi-step pipeline) and the absence of an output schema or annotations, the description covers the essentials: what it does, how it works, and what it returns (verified emails sorted by confidence). It omits potential edge cases or caveats, but remains largely complete for an agent to understand when and how to invoke it.

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 provides 100% description coverage for all three parameters, so the baseline is 3. The tool description adds minimal extra parameter-specific meaning, though it does connect the contact_name parameter to the pattern-generation process, which is a slight enhancement over 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 tool's function: 'Find email addresses for a business.' It then details the process (scraping, pattern generation, SMTP verification) and differentiates itself from siblings by framing it as a 'Hunter.io replacement' for comprehensive email discovery, distinguishing it from verify_email and verify_emails_batch.

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 offers clear context for when to use the tool (when you need to find and verify business emails) and even notes that it requires no API key. However, it does not explicitly state when not to use it or point to alternative sibling tools like verify_email, so it lacks explicit exclusions/alternatives.

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, and it does well by stating this is a read-only check ('Check', 'Shows') and detailing the exact information returned. It adds useful behavioral context (which metrics are shown) beyond the bare tool 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?

A single, well-structured sentence that front-loads the main purpose and then lists the specific outputs. 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 simplicity (no parameters, no output schema, no annotations), the description fully explains what the tool does and what the user should expect. There are no gaps in context.

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 fully covers everything. Per the baseline for 0-parameter tools, a score of 4 is appropriate since there is nothing to add.

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 ('Check') and a clear resource ('your current usage quota'), and explicitly lists what it returns (verifications used today, remaining quota, tier). This clearly distinguishes it from the sibling verification/domain 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?

The description makes the tool's context obvious: it is for checking usage quota, not for any verification task. While it doesn't explicitly say 'use this before running verifications' or mention alternatives, the use case is clear and no exclusions are needed given the sibling tools.

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 full behavioral disclosure. It explicitly details the checks performed (DNS MX, SMTP handshake without sending email, catch-all detection, disposable domains) and states the output format (confidence score 0-100). This transparently communicates both capabilities and limitations.

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 primary purpose, followed by a concise list of verification techniques. Every sentence adds value without redundancy or 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?

With a single parameter and no output schema, the description sufficiently covers behavior and return value. It explains the verification approach and the confidence score output, making the tool's overall behavior clear for an agent.

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 email parameter fully described. The description does not add additional parameter semantics beyond what the schema provides, so the baseline 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's purpose: to verify if an email address is valid and deliverable. It uses a specific verb ('verify') and resource ('email address'), and lists concrete methods (DNS MX lookup, SMTP handshake) that distinguish it from siblings like verify_emails_batch and check_domain.

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 use for single-email verification but does not explicitly mention alternatives or when-not-to-use. It does not reference sibling tools like verify_emails_batch for batch operation, so usage guidance is implied rather than explicit.

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 of disclosure. It does state that it returns verification status and confidence score for each email, which is useful. However, it does not explicitly disclose whether the operation is read-only, any side effects, authentication requirements, or rate limits. The absence of this information leaves gaps, though the basic behavior is communicated.

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 main purpose. Every sentence adds value: the first states what it does, the second describes output and use case. No fluff or 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?

The description is complete for a simple tool with one parameter and no output schema. It covers the action, limit, return value, and a use case. It does not mention error handling or behavior on invalid emails, but this is a minor gap given the simplicity and schema enforcement.

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 has 100% coverage with the only parameter 'emails' described as 'Email addresses to verify (max 25).' The description adds no additional parameter semantics beyond what the schema already provides, so the baseline 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 verifies multiple email addresses in a single batch, with a specific max limit of 25. It distinguishes itself from the singular sibling verify_email by emphasizing batch processing. The verb 'verify' plus specific scope makes 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?

The description provides clear context for when to use the tool: 'Efficient for cleaning outreach lists.' This suggests a batch use case. However, it does not explicitly mention alternatives like verify_email for single checks or state when not to use this tool, so it falls short of a 5 but is above a 3 due to the clear usage context.

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