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

With no annotations, the description carries the full burden: it does disclose meaningful behavior — what artifacts it fetches, a 0-100 scoring model, and that it auto-generates a /llms.txt and JSON-LD snippet. However, it never says whether the tool only reads or also writes/deploys the generated file, whether it makes live network requests, how long it takes, or what happens on an unreachable domain. That ambiguity around side effects is a real gap for a tool that 'auto-generates a ready-to-deploy' file.

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 the core capability and followed by the concrete inspection targets and deliverables. Some mildly promotional padding ('Complete 2026', 'ready-to-deploy') could be trimmed, but the density of useful information is high and nothing is buried.

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 covers the essential ground: what gets audited, what the score means, and what deliverables come back. The main missing piece is the response shape (score breakdown vs. generated files) and any failure behavior, but the agent has enough 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?

There is a single required parameter, and schema coverage is 100% — the schema already documents `domain` with an example. The description adds nothing about accepted formats (bare domain vs full URL with path, scheme handling) beyond what the schema states, 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?

States a specific verb and resource — scores a domain 0-100 on readiness for named AI answer engines — and enumerates exactly what it inspects (/llms.txt, /llms-full.txt, robots.txt permissions for named bots, JSON-LD entities). This is clearly distinct from sibling tools like enrich_company_domain or inspect_domain_infra, which do domain enrichment or infrastructure checks rather than AEO auditing.

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 framing ('Complete 2026 AEO/GEO auditor', 'Scores any domain') strongly implies when it applies, but there is no explicit when-to-use statement, no prerequisites (does the domain need to be live?), and no routing to or away from the sibling tools. An agent can infer the use case but must guess at boundaries.

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 exist, so the description carries the full burden. It usefully discloses parallel execution, the 25-domain cap, and the required API plan level. It says nothing about partial failures, rate limits, cost per domain, or whether a bad domain aborts the batch — significant gaps for a bulk mutation-style call.

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 sentence that packs scope, modes, cap, and the auth prerequisite with no filler. Nothing needs trimming.

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?

A simple two-parameter tool, but with no output schema the description should indicate what a per-domain result looks like and how partial failures surface across 25 parallel items. Those omissions leave an agent guessing at the response shape after it commits to the call.

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 goes beyond the enum labels by spelling out what each mode actually does (B2B enrichment, AEO scoring, DNS inspection) and restating the 25-domain cap on `domains`. That mapping of enum values to real behavior is genuine 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?

Names a specific verb+resource (bulk parallel enrichment / AEO scoring / DNS inspection) scoped to domains, and the three modes map clearly onto the single-domain siblings (enrich_company_domain, audit_ai_search_readiness, inspect_domain_infra). It does not name those siblings explicitly, so differentiation is by inference from 'bulk' and 'batch' rather than stated.

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?

Implies the usage condition (multiple domains, up to 25, in one parallel request) and discloses a plan prerequisite. It never states when to prefer this over the single-domain tools, nor what to do for batches larger than 25, leaving the batch-vs-single decision 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?

With no annotations, the description carries the behavioral burden and does well by disclosing that it is live enrichment, which pages are crawled in parallel, and what categories of data are extracted. It still omits operational traits such as rate limits, caching, authentication needs, and response shape.

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 core purpose and the remaining text is a dense but relevant enumeration of extracted data. It is somewhat run-on and includes a vague phrase like 'at the edge,' but most wording 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 one-parameter tool with no output schema, the description supplies a useful conceptual inventory of return data and crawl behavior. It is still missing response structure, limitations, and routing guidance, but it is substantially complete for basic 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% and the single domain parameter is fully documented in the schema with examples. The description does not add syntax, normalization, or validation details beyond what the schema already provides, 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 clearly states a specific enrichment operation and enumerates the extracted resources: emails, phone numbers, social profiles, tech stack, compliance certifications, and intent signals. It does not explicitly differentiate itself from siblings such as batch_enrich_domains or inspect_domain_infra, so it stops short of a 5.

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?

There is no explicit when-to-use guidance, no when-not-to-use exclusions, and no mention of alternatives. The presence of batch_enrich_domains makes the absence of routing guidance especially notable for multi-domain use cases.

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

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

With no annotations provided, the description carries the burden and does well by disclosing what is stripped (scripts, navbars, cookie banners, DOM bloat) and what is added (YAML frontmatter with title, description, canonical URL, timestamp, token metrics). It does not cover failure modes, dynamic-page handling, or rate limits, but the core transformation behavior is clear.

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 tightly written sentences front-load the action and output format, then list the cleaning behavior. Every clause adds relevant information with no repetition 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 simple one-parameter extraction tool without an output schema, the description is complete enough: it explains the input, the transformation, the output format, and the intended downstream use. No critical context is missing for an agent to decide to call it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the single url parameter is already documented as accepting a full URL or domain. The description adds only the 'live web URL' framing and does not clarify the domain-input form or any URL normalization behavior 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?

The description states a specific verb and resource: convert a live web URL into clean GitHub-Flavored Markdown. It also names the output format and frontmatter fields, making the tool's purpose immediately distinguishable from the domain-enrichment and audit 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?

The phrase 'for optimal LLM context ingestion' implies when the tool is useful, but there is no explicit when-to-use or when-not-to-use guidance. No sibling alternative is mentioned, so the agent must infer that this is the conversion tool rather than one of the analysis 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?

With no annotations, the description carries the full behavioral burden. It usefully discloses the data source (Cloudflare DNS-over-HTTPS) and that results reflect 'live' state, and 'inspect' implies a read-only operation, but it says nothing about rate limits, failure behavior for nonexistent domains, or whether lookups are cached.

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 sentence with no filler, but the long parenthetical vendor list (OpenAI, Anthropic, Stripe, Slack, Atlassian, HubSpot, Google, Microsoft 365) is padded detail that most agents do not need to select or invoke the tool.

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 tool with no annotations and no output schema, the description does the essential work of enumerating what categories of data come back, which is critical since nothing else defines the return shape. Missing only edge-case behavior and response format details.

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% for the single 'domain' parameter, so the schema already carries the semantics. The description adds nothing about acceptable formats (bare domain vs. URL, subdomains, IDN), which is the one gap an agent could plausibly hit.

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?

Uses a specific verb ('Inspect') with an enumerated resource scope (DNS, SPF/DMARC/MX, security headers, vendor verification TXT records), which is far more precise than the bare name. It implicitly separates itself from enrich_company_domain and audit_ai_search_readiness by focusing on live infrastructure records, though it never names those siblings 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?

Usage is only implied by the subject matter: an agent can infer this is for checking a domain's live DNS/mail/security posture. There is no explicit when-to-use statement, no exclusions, and no routing guidance versus enrich_company_domain or audit_ai_search_readiness, which an agent would need to choose correctly.

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