Qualisend Email Verification

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: it shows routing, timing, and spam/authentication status, and clarifies that no credit is consumed and Pro plan is required. This goes beyond the safety hints to describe what the tool actually reveals.

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 that are front-loaded with the core purpose, followed by usage instructions and constraints. Every sentence earns its place with 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?

For a simple tool with one parameter and no output schema, the description is complete. It explains what the tool does, how to invoke it, what information it returns, and its constraints (no credit, Pro plan). No critical gaps are evident.

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% coverage for the single parameter 'raw' with a description. The description adds the practical instruction 'Paste the message source / Show original', reinforcing and clarifying the expected input format, which adds value beyond the schema's baseline.

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 'Read' and clearly identifies the resource (email headers). It explains what analysis is performed (route, time per step, spam/authentication checks) and distinguishes itself from sibling verification tools by focusing on header analysis rather than address 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?

Provides clear guidance on when to use (when a message is slow or goes to spam) and how to use (paste message source). It also notes the Pro plan requirement and that no credit is used, but it does not explicitly mention alternatives or exclusions in comparison to 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?

Beyond annotations (readOnlyHint=true, destructiveHint=false), the description adds 'No credit used' (cost impact) and 'Requires the Qualisend Blacklist Monitoring add-on' (prerequisite). It also clarifies scope with named blacklists and 'point-in-time' nature, which annotations do not 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?

Two sentences: the first clearly states the purpose, the second adds cost and prerequisite. No redundancy or fluff; the most important information is 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?

With one parameter and strong annotations, the description covers purpose, scope, prerequisites, and cost. However, since there is no output schema, it could more explicitly describe the return format (e.g., boolean vs list of blacklists), though the phrasing 'whether... is currently on any' implies a binary result. Overall, sufficiently complete for a simple 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?

Schema already fully covers the single 'target' parameter with format examples ('A domain (acme.com) or an IPv4 address'). The description merely repeats 'domain or IP' without adding syntax, constraints, or parameter behavior, so it adds no value beyond 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 uses a specific verb+resource: 'Check whether a sending domain or IP is currently on any of the major email blacklists' and names concrete examples (Spamhaus, SpamCop, Barracuda). This clearly distinguishes it from sibling tools like check_domain, which likely covers broader domain reputation, by focusing on blacklist status.

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 clear context: use for a point-in-time reputation check that can affect spam placement. Also mentions prerequisites ('Requires the Qualisend Blacklist Monitoring add-on') and cost behavior ('No credit used'). However, it does not explicitly contrast with alternatives or state when not to use.

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 declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context by stating 'No credit used' and 'Available on the Qualisend Pro plan,' which go beyond annotation fields. It does not specify the exact output format, but the classification verbs make it evident.

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 action, followed by the use case and secondary notes about credit and plan. Every sentence earns its place with 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?

For a one-parameter read-only tool with no output schema, the description covers purpose, use case, credit behavior, and plan requirement. The lack of an explicit return format is minor because 'Tell whether' implies the classification result, making the description sufficient for selection and invocation.

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%—the only parameter 'email' is described as 'The email address to check.' The description's classification explanation does not add parameter-level detail beyond the schema, so 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 states a specific verb+resource: 'Tell whether an email address is a throwaway/temporary one, a generic team inbox...or a free consumer address.' It clearly identifies the classification task and distinguishes it from siblings like check_blacklist or check_domain by focusing on disposable/generic/free detection.

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 a clear use case: 'useful for spotting low-quality sign-ups.' This implies when to use the tool, but it does not explicitly name alternatives or state when not to use it, so it stops 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral details: it explicitly says 'No credit used' (a cost implication) and that the check reports 'what's missing' in sender authentication. It does not contradict any annotation and adds useful context beyond the structured data.

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 short sentences, with the purpose front-loaded. The first sentence captures what the tool does, the second restates it as a 'health check' (slight redundancy but reinforces clarity), and the final two sentences provide cost and plan info. Every sentence earns its place with no 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?

For a simple single-parameter read-only tool, the description covers purpose, scope, cost, and plan restriction. It hints at output behavior by noting 'what's missing,' which gives some expectation of the result. It does not describe the exact output format, but given the simplicity and the absence of an output schema, the description 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?

Schema coverage is 100%: the single 'domain' parameter is described as 'The domain to check, e.g. acme.com.' The description confirms 'a sending domain' but does not add extra formatting details or examples beyond the schema. With full schema coverage, 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 uses the specific verb 'Check' with the resource 'a domain's email setup' and further details the scope: 'mail servers plus its SPF and DMARC sender-authentication, and what's missing.' This distinguishes it from sibling tools like verify_email (which checks individual addresses) and check_blacklist (which checks reputation). The purpose is unambiguous and specific.

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 phrase 'a plain deliverability health check for a sending domain' provides clear context for when to use this tool. It also notes 'No credit used' and 'Available on the Qualisend Pro plan,' which are practical usage constraints. It does not explicitly state when not to use it or name alternatives, but the context is sufficient for common 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable operational context not in the annotations: 'No credit used' and 'Available on the Qualisend Pro plan.' It also clarifies that the tool 'suggests' a correction, aligning with read-only 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 two sentences, front-loaded with the core action, followed by an example, benefit, and operational notes. Every sentence adds value with no redundancy or 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?

The tool is simple with one parameter, and the description conveys the essential behavior. However, it does not specify the return format or what happens when no typo is found, and there is no output schema to compensate for this ambiguity.

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 the email parameter is already described as 'The email address to check for a typo.' The description adds a concrete example (gmial.com) that helps the agent understand what constitutes a typo, going slightly beyond the schema baseline.

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 verb and resource: 'Spot a likely typo in an email's domain' and 'suggest the correct one.' The example 'gmial.com' and benefit 'so you don't lose contacts to misspelled addresses' distinguish it from sibling verification tools such as verify_email or 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 provides a clear use case ('so you don't lose contacts to misspelled addresses') that implies when to use the tool. However, it does not explicitly contrast with alternatives or state when not to use it, lacking exclusions or when-not 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 already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read-only operation. The description adds valuable behavioral context: it returns a copyable record with placement instructions, uses no credit, and requires a Pro plan. This goes beyond what annotations provide, covering return format and constraints. No contradiction with 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 compact: one sentence for purpose, one for output, and one line for caveats (no credit, Pro plan). It's front-loaded with the core purpose, then return details, then constraints. No redundant wording; 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?

For a tool with 14 parameters and no output schema, the description sufficiently explains what it does, what it returns, and important constraints (credit, plan). Parameter details are fully delegated to the schema, which is acceptable. The only gap is not clarifying parameter dependencies (e.g., BIMI requires logoUrl), but that's beyond the description's role.

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 all 14 parameters (100% coverage), including enums and descriptions, so the schema does the heavy lifting. The description itself adds no parameter-specific semantics beyond naming the four record types, which maps to the 'type' parameter. Per rubric, baseline 3 applies when schema coverage is high; no additional value from the description.

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 names a specific verb ('Create') and resource ('email-authentication DNS record'), explicitly enumerates the four record types (SPF, DMARC, BIMI, MTA-STS), and explains the output. This clearly distinguishes it from sibling verification/checking tools, which focus on analysis rather than generation.

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 states a clear context: 'so its email is trusted and harder to spoof', and lists the record types it supports. It doesn't explicitly name alternatives or exclusion criteria, but the sibling list (all verification/checking tools) makes it obvious this tool is for generating DNS records, not verifying. Clear context without explicit exclusions warrants a 4.

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 declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds workspace scope ('Qualisend workspace') but does not disclose any additional behaviors such as rate limits, data freshness, or potential errors. This is acceptable for a simple read-only tool, meeting the minimum bar.

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 conveys all essential information without waste. It is front-loaded with the action and outcome.

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, read-only tool with strong annotations, the description is complete. It states what the tool returns and the scope, leaving no critical gaps for an agent to invoke it correctly.

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 no parameters and schema coverage is 100%, so the description does not need to explain parameter semantics. The description adds context about what is being returned, which is sufficient.

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 ('Return') and clearly identifies the resource ('number of verification credits in your Qualisend workspace'). It distinguishes this tool from siblings, which are focused on email verification actions, leaving no ambiguity about the tool's function.

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 the tool is used when you need to check available credits before performing verification tasks. There are no explicit exclusions or alternatives, but given that no sibling tool serves this purpose, usage context is 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds meaningful behavioral context by explaining the different return types for single vs batch jobs ('final verdict once the mailbox probe completes' vs 'progress + a result-mix summary'). This goes beyond the annotations and clarifies what to expect in the response, though it does not discuss error handling or timing details.

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 concise sentences, front-loaded with the action and resource, and immediately elaborates on expected behavior. Every sentence earns its place, with no fluff or repetition of schema 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?

For a simple lookup tool with one parameter and no output schema, the description is complete. It explains the purpose, the input source, and the output behavior for both single and batch cases. The rich sibling context and annotations further reduce ambiguity, making this sufficiently complete for an agent to use correctly.

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 schema already describes job_id as 'The job_id returned by a verify tool.' The description repeats this information, adding little new value. It reinforces the source but does not provide additional syntax, format, or usage details beyond the schema, so 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 'Look up the status and result of a verification you started' with a specific resource (job_id). It distinguishes itself from sibling verification-starting tools by referencing job_id from verify_email/verify_emails and explaining single vs batch behavior, making its 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 implies usage after starting a verification by referencing 'verification you started' and job_id from verify tools. It does not explicitly name alternatives or exclusions, but the context is clear enough that an agent would know to use this for retrieving results, not for initiating checks. A minor gap is the lack of explicit 'use this only after a verification has been started' 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 already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral details: it states that no credit is used and that the tool is available on the Pro plan. It also clarifies the output behavior (whether the code is temporary or permanent), which goes beyond the annotation-only baseline.

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 concise sentences, front-loaded with the main purpose and output. The second sentence adds cost and plan context without redundancy. Every word earns its place, and there is no 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?

For a simple single-parameter lookup tool with rich annotations and no output schema, the description is fully complete. It explains what the tool does, what the output will be (plain English and classification), cost implications, and availability. It also implicitly differentiates from all sibling tools. No coverage gaps remain.

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 schema already describes the 'code' parameter with examples. The description repeats these examples ('550 or 5.1.1') but does not add new semantic information beyond the schema. Thus it meets the baseline for high coverage without compensating 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 verb 'Explain' and the resource 'mail-server reply/error code', and specifies the output: plain English explanation and a temporary/permanent classification. It also distinguishes itself from sibling tools by focusing on code lookup rather than verification or DNS checks.

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 gives clear context—use this when you need to understand an SMTP error code—and mentions the Pro plan requirement, which helps an agent decide if the tool is available. It does not explicitly exclude alternative tools, but the sibling tools are clearly different in purpose, so the usage scenario is well 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?

Annotations don't provide readOnly/destructive guarantees; description adds important behavioral context: consumes 1 credit and returns a deliverability verdict. This goes beyond what annotations and schema 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?

Two sentences, front-loaded with action and resource, and every word adds value. No filler 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?

Covers purpose, scope, return value, and cost. Missing edge-case handling details but is adequate for a single-parameter tool. No output schema exists, so describing the verdict is useful.

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 the only parameter (email) completely. The tool description adds no extra detail about parameter syntax or formatting, 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?

Clearly states 'Verify a single email address' and enumerates specific components (syntax, domain/MX, disposable, role, typo, mailbox reachability), distinguishing it from narrower siblings like check_disposable or check_typo.

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 indicates it verifies a single email address but does not explicitly state when to use this tool over alternatives like check_disposable or verify_emails. Usage context is implied, not 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?

Annotations are sparse (readOnlyHint=false, destructiveHint=false, openWorldHint=true), and the description adds useful behavioral details: 'Consumes 1 credit per address' and 'synchronously.' This goes beyond what annotations convey, though it doesn't cover potential side effects or failure modes. The cost and synchronous nature are transparent and valuable.

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 no fluff. The first sentence states the core action and output; the second adds cost and an alternative for larger lists. Information is front-loaded and 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?

The tool is simple (1 param, no output schema), and the description covers the main purpose, batch limit, cost, synchronous behavior, and alternative for larger lists. It implies the return value ('deliverability verdict for each') and is complete enough for straightforward use. It lacks explicit error handling or return format details, but these are not critical given the 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?

The schema already provides full coverage for the single 'emails' parameter (description: 'Email addresses to verify (1–50)'), so the baseline is 3. The description adds semantic context by noting the 50-email upper bound and per-address credit consumption, which clarifies the parameter's cost and scale implications. This is a meaningful addition beyond 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 identifies the tool's function: 'Verify up to 50 email addresses at once and get a deliverability verdict for each, synchronously.' It specifies the resource (email batch) and the output (deliverability verdict), and distinguishes it from sibling verify_list by explicitly noting the size limit and alternative.

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?

It states when to use this tool: for batches up to 50, and provides an explicit alternative for larger lists: 'For larger lists (up to 1000), use verify_list.' This gives clear usage context and a specific alternative, satisfying the when/when-not/alternatives criterion.

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 description discloses asynchronous execution, creation of a single bulk job, immediate job_id return, background mailbox probes, credit consumption per address, and result retrieval via get_verification. These details go far beyond the basic annotations and provide critical operational context 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?

Four concise sentences cover purpose, async behavior, polling instructions, credit cost, and size limit without any fluff. The information is front-loaded and 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?

Despite having no output schema, the description explains the job_id return, how to obtain results via get_verification, and the result summary counts. The async flow, cost model, and limits are fully specified, making the tool self-sufficient for an agent to select and invoke correctly.

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 documents the sole 'emails' parameter with item type and maxItems=1000. The description reiterates the limit but adds no additional parameter-level semantics beyond what the schema provides, so the baseline 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 opens with 'Verify a large list of up to 1000 email addresses,' using a specific verb, resource, and explicit scope. It clearly distinguishes itself from siblings like verify_email by emphasizing bulk processing and immediate asynchronous execution.

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 instructs polling get_verification with the job_id for progress and directs users to the dashboard or bulk API for lists larger than 1000. This gives clear when-to-use and alternative guidance, leaving no ambiguity about the tool's role.

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