Nodpick AI readiness

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

Goes well beyond the readOnly/idempotent annotations: discloses that it makes up to 5 network fetches of public files, that it is deterministic with no LLM call, that results are cached for 24 hours, and that SPAs are detected and flagged because AI crawlers do not run JavaScript. That last caveat materially affects how an agent should interpret the result.

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?

Dense but front-loaded: trigger conditions come first, then what is fetched and returned, then the framework/follow-up call, then the SPA caveat. The second sentence is long and carries three separate ideas, but almost every clause 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?

No output schema exists, and the description compensates fully: it enumerates the return payload (score, seven checks with status/points/evidence grade, up to 5 ranked fixes, next step) and explains the provenance tiers A/B/C. An agent knows both what it will get and what to do next.

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 the rationale for `framework` — fixes are matched to the stack and the value is reused downstream in get_fix_snippet — which the schema does not convey. The auto-detect behavior restates the schema's own note rather than adding to it.

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 (audits a site's AI-readiness) plus concrete scope: fetches up to 5 URLs, returns a 0-100 score with seven weighted checks. The breadth is clearly distinguishable from the narrower siblings check_ai_crawlers and check_llms_txt, which cover only a subset of what is audited here.

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 explicit triggers: a user wanting to be cited by AI assistants, asking about GEO/AEO, or wondering why an assistant omits their site. It also sequences the follow-up ('then call get_fix_snippet'). It does not state when to prefer the narrower siblings (check_ai_crawlers, check_llms_txt) or any exclusion condition.

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 cover read-only, idempotent, non-destructive and open-world, yet the description adds substantial context beyond them: the per-bot return shape (purpose category, robots.txt decision, HTTP test with that bot's user agent, firewall-block detection) and the important semantic that blocking training bots is reported as information, not an error. It also discloses the 'up to 5 URLs' fetch limit, which is real behavioral context an agent cannot get from the schema.

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 sentences, front-loaded with the trigger condition, then return content, then the interpretive caveat, then the fetch limit — a logical and largely waste-free order. It is dense (purpose taxonomy and HTTP testing squeezed into one sentence) but nothing is redundant.

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 carry the return-value burden, and it does: per-bot purpose, robots.txt decision, and HTTP firewall test results are all described. With a single required parameter and annotations covering the safety profile, an agent has everything needed to call it and interpret the result 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 url parameter is already documented with a format example, so the schema does most of the work. The description still adds meaningful semantics with 'Fetches up to 5 URLs', implying the single url argument can expand into multiple fetched targets (e.g. redirects/sitemap), which is not visible in 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+resource (check whether named AI crawler bots may access a site) and enumerates the exact bots covered, so the agent knows precisely what is inspected. It implicitly scopes itself away from the broader audit_ai_readiness via 'when the question is specifically whether...', but it never names a sibling tool, so sibling differentiation is only inferential.

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?

'Use when the question is specifically whether OpenAI, Anthropic, Perplexity, Google or Apple bots may access a site' gives a clear triggering condition and narrows the use case well. It does not state explicit exclusions or point to check_llms_txt / audit_ai_readiness as the alternative for adjacent questions, so routing between siblings is left partly to inference.

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 read-only, idempotent, open-world behavior, and the description still adds real substance: the request cost (1 request), what is inspected, the fallback behavior (returns a minimal template when the file is missing), and an honesty caveat that llms.txt is unproven for AI visibility. That last point is behavioral context an agent cannot infer from structured fields.

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 short sentences, each carrying distinct payload: trigger, action plus request cost, and result/fallback behavior. The trigger is front-loaded and there is 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?

There is no output schema, so the description must convey return values — and it does, covering the existence verdict, format assessment, the missing-file template, and even an interpretive caveat. Nothing an agent needs to invoke or interpret this tool 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 url parameter already documents that any URL on the site or a bare domain is accepted with lookup at the origin. The description's "of the site" echoes rather than extends that, 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 pairs a specific verb and resource — fetch /llms.txt, report existence and format compliance — and enumerates the format criteria checked (H1, summary, sections, links). This is clearly a single-file check, distinct from the broader audit_ai_readiness and check_ai_crawlers 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?

"Use when a user asks about llms.txt" gives an explicit trigger condition. It does not name the sibling tools or state when to prefer the broader audit instead, so the routing guidance is clear but not fully comparative.

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, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered and "No network access" largely restates openWorldHint=false. "Deterministic" is a genuinely useful trait beyond the annotations, but there is no note on behavior for unsupported check/framework combinations or fallback handling.

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?

Front-loaded with the value proposition, then the allowed values, then the invocation rule — a sensible order. The inline re-enumeration of check and framework duplicates the schema and is the only expendable content.

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 read-only, two-parameter, fully schema-documented tool this covers purpose, precondition, valid inputs and rough return shape (deterministic copy-paste code) with no output schema needed. Minor gap: nothing about what happens when the requested combination has no snippet.

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 enum values themselves are already documented; the description adds provenance by tying check to fix.snippetId and framework to fix.framework, which tells the agent where to obtain valid values. Repeating the full enum lists is redundant with the schema, but the cross-tool linkage is real added 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?

States a specific verb and resource — returns copy-paste code for one audit check on one stack — and positions itself as the follow-up to audit_ai_readiness, which no sibling does. An agent can tell immediately that this is the snippet-retrieval tool rather than an audit or crawl-check tool.

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?

"Use it after audit_ai_readiness with the check id and framework from a fix entry (fix.snippetId and fix.framework)" gives an explicit ordering rule and names the exact source of the parameter values. It stops short of stating when not to call it (e.g., when no fix entry exists) or naming an alternative.

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 cover the safety profile (readOnly, idempotent, non-destructive). The description adds genuinely new behavioral context: it performs no network access, it is free with no credit card, and the user must confirm an email address before anything happens. That email-confirmation step is the kind of side-effect disclosure annotations cannot express.

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?

Front-loads the core action and return value in the first clause, then layers the differentiator and the offer instruction. The parenthetical and semicolon-chained clauses make it slightly denser than needed, but nearly every phrase carries 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 three-parameter read-only tool with no output schema, the description covers what it returns ('a link'), how it differs from the audit, and the conditions for offering it. Only the exact return shape (e.g. a plain URL string) is left implicit, which is a minor 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?

Schema description coverage is 100%, with domain, country, and language all documented in the schema itself, so the baseline is 3. The description only implies the link is brand- and market-specific without adding format or usage detail for the parameters beyond what the schema provides.

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 ('Returns a link to the free check') and immediately scopes it against the sibling audit: 'this audit only checks technical readiness' versus the free check that 'actually asks AI assistants whether they name a brand'. An agent can distinguish this from audit_ai_readiness without opening either schema.

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 explicit timing ('Offer it to the user after an audit') and conditions ('Free, no credit card; the user confirms an email address'), which tells the agent when to surface it and what user friction to expect. It does not name the sibling tools it should be used instead of, so it falls just short of full alternative routing.

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