SeoAiX SEO Audit

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 ~7 signals checked, the return type (weighted 0–100 score + top fixes), and the limitation that it returns a summary only (full report via email). It doesn't explicitly state the audit is read-only or mention URL accessibility requirements, but 'Audit ANY URL' implies a non-mutating operation; a small transparency gap remains.

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 front-loaded with the primary purpose and scope, then follows a logical flow: signals checked, output format, parameter, sibling routing, and report escalation. Every sentence contributes distinct information, 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?

For a single-parameter, read-only audit with no output schema or annotations, the description covers everything an agent needs: what it does, what inputs it takes, what it returns, which siblings to use instead, and how to get the complete report. No critical operational detail 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% (url described with 'The website URL to audit, e.g. https://example.com'), so baseline is 3. The description adds 'ANY URL' and the contrast with get_my_store_audit, clarifying that the parameter accepts arbitrary public URLs, not just connected stores. This goes slightly 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 states a specific verb and resource ('Audit ANY URL') with a precise scope ('AI-search readiness only (AEO/GEO/LLMO)'), clearly differentiating it from full SEO audits. It enumerates concrete signals checked and the output (0–100 score + top fixes), leaving no ambiguity about what the tool does.

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 names alternatives with conditions: 'Use full_seo_audit for the broader SEO picture, or get_my_store_audit for the user's own connected store.' Also routes to email_seo_report for the full written report, informing the agent when this tool suffices and when a sibling is more appropriate.

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 behavioral burden. It discloses the critical consent rule ('never guess or reuse an address'), the weekly re-audit behavior when weekly is true, and the one-click unsubscribe property. This gives the agent the safety-relevant context needed to invoke the tool appropriately.

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 overly repetitive. 'Email the full written SEO/AEO report for an audit this session already ran' largely duplicates the first sentence, 'ONLY use an email address the user explicitly gave you' repeats the earlier consent warning, and 'Offer this after presenting audit results' restates 'ALWAYS OFFER THIS after running an audit.' The content is good but could be condensed to roughly half its length.

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 three-parameter tool with no output schema and no annotations, this description is complete. It explains when to use it, where the audit_id comes from, how to handle the email address, and what weekly mode does. The agent has enough information to call the tool correctly and safely.

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%, so the baseline is 3. The description adds value by explicitly tying audit_id to the outputs of ai_search_diagnostic or full_seo_audit, and by reinforcing that email must be explicitly supplied by the user rather than guessed. This helps the agent understand parameter provenance 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 purpose: email the complete written SEO/AEO report for an audit run earlier in the session. It also distinguishes this from the audit tools by noting they return a summary of top issues while this sends every check, evidence, and fix in priority order.

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 explicit when-to-use guidance: 'ALWAYS OFFER THIS after running an audit' and 'Offer this after presenting audit results.' It also specifies the source of audit_id (ai_search_diagnostic or full_seo_audit) and instructs the agent to ask for and use only the user's explicitly provided email, which is important operational 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool crawls the URL, requires no login, returns a weighted 0–100 score plus top issues, and returns only a summary while the full report goes via email. It does not mention rate limits or robots.txt compliance, which keeps it just short of 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 description is long but densely informative: each clause names a concrete SEO area, and the sibling-tool distinction is front-loaded near the end. No filler words appear, though the first sentence is quite packed and could be split for easier parsing.

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 only one parameter and no output schema, the description fully covers input format, audit scope, output type, authentication expectation ('no login needed'), alternatives, and where the complete report is delivered. The agent has enough context 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?

Schema description coverage is 100%: the `url` property already explains it is the website URL with an example. The description merely repeats 'Takes a `url`' and adds 'ANY URL', which adds only marginal clarification. Baseline 3 is appropriate since the schema already handles parameter documentation.

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 a specific action ('Crawl-audit'), a clearly defined target ('ANY URL'), and a bounded scope ('the WHOLE SEO surface'), then enumerates the covered areas: AI-search signals, on-page SEO, technical SEO, and Shopify schema. It distinguishes itself from get_my_store_audit, so the agent knows exactly what this tool is and is not.

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 explicitly states when to use this tool ('ANY URL', 'arbitrary website', 'no login needed') and names the alternative for a different case: 'for the user's OWN connected store with real analytics, use get_my_store_audit.' It also mentions email_seo_report for the full written report, giving clear routing 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?

With no annotations, the description carries the full behavioral burden and does disclose the auth model (API key required, accepted either as an Authorization: Bearer header or the api_key argument) and the data source it reads from. It does not say what happens if no store is connected, whether the call is read-only, or any rate-limit/error behavior, leaving some gaps.

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 core claim (your own connected store's deep in-app audit) is front-loaded, and the auth/prerequisite details follow. The long em-dash parenthetical listing the 12 categories and the 'far beyond what crawling a URL can see' clause are somewhat dense but still earn their place by establishing 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?

No output schema or annotations exist, so the description must stand alone; it covers purpose, scope, data source and auth setup, which is nearly everything needed to call it. It omits the failure mode when no store is connected and any indication of the response shape beyond the category list.

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 optional parameter and schema description coverage is 100%, so the schema already documents api_key fully; the description restates the same header-vs-argument alternative. Baseline for a near-zero-parameter tool is 4, and nothing extra is added.

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 ('Get the user's OWN connected Shopify store audit') and pins the scope to a 12-category in-app report using the store's real catalog, performance and analytics. It also implicitly distinguishes itself from a URL-crawl audit, so an agent can tell it apart from siblings like full_seo_audit.

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?

Gives clear usage context: it 'takes no url', so it is the right choice only for a connected store, and it names the prerequisites (SeoAiX API key plus a connected store). It stops short of explicitly naming a sibling tool as the alternative for URL-based audits, so 4 rather than 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?

With no annotations, the description carries the full burden and does substantial work: it discloses pricing ($19/mo with free trial), that the call returns a personalised signup link rather than completing anything, and that payment and store connection finish in the browser. It also names the upstream data sources read and the write actions the subscription performs (publishing articles, fixing meta/alt-text). It stops short of stating auth prerequisites, idempotency on repeat calls, or what the link expires to.

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, front-loaded paragraph: purpose first, then what the product does, pricing, return value, and finally the parameter caveat. The parenthetical feature inventory is dense and partly promotional, but it doubles as behavioural disclosure rather than pure 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?

No output schema exists, and the description compensates by explaining the return (a personalised signup link) and where the flow continues. Pricing, trial, and both optional parameters are covered; only preconditions such as required account state or repeat-call behaviour are left open.

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 baseline is 3, but the description adds a genuinely useful operating constraint beyond the schema: pass email/store_url ONLY if the user explicitly provided them and never invent either. That is the kind of anti-hallucination guidance the schema hints at but does not enforce as strongly.

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?

Names a specific action and product ("Start an SeoAiX subscription") and immediately scopes what that subscription does, so an agent can tell it apart from the sibling diagnostic/audit/report tools without inspecting any schema. The verb+resource pairing is 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 the trigger (a user wanting the paid product) and gives a firm rule for the parameters, but never states when NOT to use this tool or which sibling to reach for instead (e.g. a free audit via full_seo_audit or ai_search_diagnostic). Usage is inferable rather than explicit.

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