Qualisend Email Verification

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

Annotations already establish readOnlyHint=true, destructiveHint=false and closed-world scope, so the safety profile is covered. The description adds genuinely non-structured context: no credit is consumed and the feature requires the Qualisend Pro plan, which affects whether the agent should even attempt the call. It does not mention rate limits or input size limits, keeping it from a 5.

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 value proposition and the usage scenario are front-loaded in the first sentence, with operational notes (input source, no credit, plan) following. It is somewhat dense with parenthetical asides, but every clause carries information an agent can act on.

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?

There is no output schema, so the description must convey what comes back, and it does: transit route, per-step latency, and spam/authentication check results. For a read-only, single-parameter tool with annotation coverage and an explicit plan/cost note, nothing an agent needs to call it correctly is missing.

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 a single parameter at 100% schema description coverage, the schema already documents 'raw'. The description reinforces this with 'Paste the message source / Show original', which adds practical guidance for locating the value but no new format or syntax detail. This is the expected baseline when the schema carries the load.

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 and resource ('Read the hidden technical details of an email (its headers)') and enumerates the concrete outputs: route taken, per-hop timing, and spam/authentication results. This clearly separates it from siblings like verify_email and check_blacklist, which do not analyze an existing message's transit path.

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 gives a clear triggering scenario ('handy for figuring out why a message was slow or went to spam') and tells the agent what input to supply ('Paste the message source / Show original'). It does not explicitly name alternatives or state exclusions, 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?

Beyond the readOnlyHint and destructiveHint annotations, the description discloses the add-on requirement, that no credit is used, and critically explains the semantics of `listed_on`, `undetermined_on`, and `clean`. The warning that a non-empty `undetermined_on` means UNKNOWN—not clear or listed—is essential behavioral context.

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?

Three sentences with no filler: the first states purpose, the second adds requirements and cost, and the third delivers the critical interpretation caveat. Every sentence earns its place, and key info 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?

For a one-parameter tool with no output schema, the description fully covers what an agent needs: how to interpret ambiguous results, what `clean` really means, and operational constraints like the add-on requirement. Nothing essential is missing.

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% for the single `target` parameter, and the description mainly echoes the domain-or-IPv4 value type. Since the schema already documents the parameter fully, this is the appropriate baseline; the description adds no new parameter-level 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 ('Check') and resource ('a sending domain or IP against email blacklists'), naming concrete lists like Spamhaus, SpamCop, and Barracuda. It clearly positions this as a point-in-time reputation check and differentiates it from sibling tools like check_disposable 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?

It gives clear context: use this to check whether a sender is on major spam-affecting blacklists, as a point-in-time reputation check. It does not explicitly state when not to use it or name alternative tools, but the intended use case is clear enough relative to the sibling list.

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 establish this is a safe read-only, non-destructive, closed-world operation. Beyond that, the description adds two genuinely useful behavioral facts: 'No credit used' (cost) and 'Available on the Qualisend Pro plan' (plan gating), neither of which is derivable from the schema or 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?

Two compact sentences, front-loaded with the core purpose and followed by the two operational notes. Every clause carries information; no filler or repetition.

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, read-only lookup with no output schema, the description covers purpose, the categories returned, cost, and plan availability. The only mild gap is that it does not describe the shape of the result beyond the category names, but that is largely implied.

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?

There is a single required parameter with 100% schema description coverage, so the schema fully documents the input. The description adds no format, syntax, or validation detail (e.g., normalization of the email) beyond what the schema already provides, which is the baseline 3 case.

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 and resource ('Tell whether an email address...') and enumerates the three distinct categories it detects (throwaway/temporary, generic team inbox, free consumer), with concrete examples. It is clear and self-contained, though it never explicitly differentiates itself from siblings like check_domain 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?

It offers a use case ('useful for spotting low-quality sign-ups'), which implies when the tool is relevant, but gives no explicit when-to-use/when-not guidance and does not name any alternative. An agent must infer that this is distinct from check_domain or verify_email.

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 establish readOnlyHint=true, destructiveHint=false and openWorldHint=true, so safety is covered. The description adds genuinely useful non-structured context: no credit is consumed and a Pro plan is required, plus an implicit sketch of what the response reports ('what's missing').

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 compact sentences, front-loaded with the core check and followed by scope qualifiers. The second sentence partly restates the first ('plain deliverability health check'), a minor redundancy, but nothing is padded out.

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 required parameter, full schema coverage, annotations covering the safety profile, and no output schema to explain, the description supplies what an agent needs: domain-level scope, what is evaluated, cost, and plan gating. Only the lack of routing to sibling tools leaves a small gap.

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?

There is a single parameter with 100% schema description coverage, so the schema already documents the domain argument and its format. The description adds no syntax or normalization guidance beyond it, making the baseline of 3 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 names a specific verb (check) and resource (domain) plus the concrete facets examined: mail servers, SPF, DMARC, and gaps. It implicitly separates itself from mailbox-level siblings like verify_email, though it never names an alternative explicitly.

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 frames the use case ('a plain deliverability health check for a sending domain') and adds plan and cost context ('No credit used', 'Qualisend Pro plan'), which helps an agent decide when it is callable. However it gives no when-not guidance and does not point to verify_email or check_blacklist for adjacent questions.

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?

Beyond the readOnlyHint/destructiveHint/openWorldHint annotations, the description discloses that it returns the record plus where to place it, that no credit is consumed, and that it requires the Pro plan. The read-only framing ("record to copy into DNS") usefully clarifies the "Create" verb rather than contradicting the annotations; it stops short of describing pagination or per-type output 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?

Three tight sentences, front-loaded with the capability and record types; the return behavior, cost, and plan constraint each earn their place. 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?

For a 14-parameter tool with no output schema, the description compensates by stating what is returned ("the record to copy into DNS, with where to put it"). Combined with 100% schema coverage, an agent has what it needs; only per-type invocation nuance is left implicit.

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% across all 14 parameters, so the schema already carries full parameter meaning (including per-type scoping like "SPF:", "DMARC:", "BIMI:"). The description adds no syntax or format guidance beyond naming the record types already exposed in the enum, 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?

States a specific verb+resource ("Create the exact DNS setting") and enumerates the four supported record types (SPF, DMARC, BIMI, MTA-STS), which map directly to the `type` enum. An agent can immediately tell this generates DNS records for email authentication, cleanly distinct from the verification/lookup siblings.

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?

Usage is only implied by the framing ("so its email is trusted and harder to spoof") and the plan note. It never states when to pick this over sibling verification tools or when one record type is appropriate versus another, so the agent must infer intent from 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 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 mark this non-read-only and open-world, but the description adds the crucial cost trait ('Consumes 1 credit') that annotations cannot express, plus the enumerated verdict domain (deliverable / risky / undeliverable / unknown). It does not mention latency or rate limits, but the credit disclosure is a real value-add.

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 tight sentences: capability/scope first, then return contract and cost. Every clause earns its place with no filler or repetition of the title.

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 no output schema, the description does supply the verdict vocabulary an agent needs to branch on. It stops short of describing the remaining payload (e.g. typo suggestion text, MX records) and there is no output schema to fill that gap, but the primary decision surface is covered.

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?

There is one parameter at 100% schema description coverage ('The email address to verify'), so the schema already carries the semantics. The description adds nothing about accepted formats or normalization, which is the baseline expectation at this coverage level.

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 and resource ('Verify a single email address') and enumerates the exact checks performed (syntax, domain/MX, disposable, role, typo, mailbox reachability). The word 'single' cleanly separates it from the bulk siblings verify_emails and verify_list without opening their schemas.

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?

Usage is implied: pick this when you need a full deliverability verdict on one address. However, it never says when to prefer a narrow sibling like check_domain, check_disposable, or check_typo, nor when to escalate to verify_emails for batches, leaving the agent to infer the boundary.

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=false, openWorldHint=true, destructiveHint=false. The description adds two things annotations cannot carry: the call is synchronous, and it consumes 1 credit per address. No mention of what happens on partial failure or whether invalid addresses consume credits.

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 short sentences, zero filler. Scope and the alternative routing are front-loaded before any detail.

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 limit, synchronous behavior, credit cost, escalation path, and the nature of the return value ('deliverability verdict for each') despite no output schema. Nothing an agent needs to call this correctly is missing.

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% and the single parameter is fully documented with its 1-50 range, matching the description's 'up to 50'. The description adds no syntax or format guidance (e.g. duplicates, malformed input handling), so 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?

States a specific verb ('verify'), the resource ('email addresses'), the batch cap (50), and the synchronous verdict output in one sentence. It is immediately distinguishable from verify_list by scope.

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 routes bulk work to verify_list with the threshold ('larger lists (up to 1000)'). It does not mention the single-address sibling verify_email, so the boundary on the small end is left implicit.

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?

Adds substantial behavior beyond the annotations: it runs asynchronously, creates exactly ONE bulk job, returns a job_id immediately while probes run in the background, and consumes 1 credit per address. That credit cost and the one-job-per-call guarantee are exactly the kind of context annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=false) cannot 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?

Four tight sentences, front-loaded with the async behavior and job_id return, then polling, cost, and the overflow path. No sentence is 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?

Without an output schema, the description still tells the agent what comes back immediately (job_id), where the actual results live (get_verification, counts by verdict), cost, and the over-limit fallback. Nothing needed to invoke it correctly is missing.

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?

Only one parameter and the schema already documents it at 100% coverage (array of strings, 1-1000, matching maxItems). The description restates the 1000 bound and adds the per-address credit cost, but adds no new syntax or format detail; baseline 3 applies when the schema does the heavy lifting.

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 a specific verb and resource with scope: 'Verify a large list of up to 1000 email addresses.' The word 'large' plus the 1000 cap distinguishes it from the single-address verify_email and the smaller verify_emails siblings.

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?

Explicit routing: use this for lists up to 1000, 'For lists larger than 1000, use the Qualisend dashboard or bulk API.' It also names the follow-up tool (get_verification) and the condition that triggers it (polling for progress).

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