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

No annotations exist, so the description carries the full behavioral burden and falls short. It does not say whether the tool writes files to a host, returns text blobs for the caller to deploy, or requires auth/domain verification — a critical distinction for a 'production-ready' artifact generator. No output schema exists to cover the return shape either.

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 the verb, all four output artifact types, and the target audience with zero filler. Nothing here can be cut without losing 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?

The description is complete about WHAT artifacts are produced, which is the most important piece. However, for a seven-parameter, five-required tool with no annotations and no output schema, it leaves open how inputs shape the artifacts and whether output is files or text — gaps an agent must guess at before calling.

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 only 43%, and the description adds nothing about the seven parameters — notably the five required ones (brand_name, domain_url, industry_context, products_or_services, official_facts) and the unexplained 'lang' default of 'es'. With low coverage the description should compensate with input guidance such as fact formatting or URL expectations, and it does not.

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 ('Synthesize') and enumerates four concrete artifacts (/llms.txt, ai-catalog.json, Schema.org JSON-LD, robots.txt rules) for a specific audience (AI crawlers). This is unmistakably a generator, distinguishing it from audit/probe/query/remediate siblings by implication, though no sibling is named 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: 'production-ready' suggests it belongs at the publishing/deployment stage rather than during diagnosis. There is no explicit when-to-use, when-not-to-use, or routing against dante.run_audit or dante.remediate_hallucination, which an agent choosing among five GEO tools would benefit from.

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 behavioral burden. It identifies the engines measured but does not disclose important behavior such as cost implications of live queries, authentication requirements, rate limits, or what the probe actually returns.

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 definition is a single front-loaded sentence with no filler. It is appropriately sized for a tool description and every phrase contributes to the stated purpose.

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 5-parameter tool with no annotations and no output schema, this description is under-specified. It does not explain expected outputs, how Share of Model is computed, what live=false means, or how the remaining undocumented parameters affect behavior.

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 only 60%, and the description adds no parameter-level meaning. It mentions target metrics and engines but does not explain target_name, industry_or_context, lang, live, or vertical_mode beyond what the schema partially 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 uses a specific verb, 'Measure,' and names the exact metrics (GEO Share of Model and First-Choice Win Rate) plus the AI engines covered. It is clear what the tool does, but it does not explicitly distinguish this tool from siblings such as dante.forge_geo_shield or dante.run_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?

The description states what is measured but gives no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or sibling tools, leaving usage selection 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?

No annotations are provided, so the description carries the full behavioral burden. It mentions tenant isolation and the data domain, but it does not describe search behavior, permissions, rate limits, pagination, or return characteristics. For a query tool with zero annotation coverage, this is a significant gap.

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, front-loaded sentence with no filler. It efficiently states the core action, data source, and signal categories.

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?

Given no annotations, no output schema, and an incomplete schema description coverage, the description should provide more context about result format, search behavior, and parameter usage. It identifies the data domain but leaves too much unspecified for an agent to invoke the tool confidently.

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 description does not mention any of the three parameters. Schema description coverage is only 67%, and the voice_filter parameter has no schema description at all. The description therefore fails to compensate for the undocumented parameter semantics.

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 ('Search') and a specific resource ('isolated tenant intelligence vault'), and lists the kinds of signals it can find. It is clear what the tool does, but it does not explicitly distinguish itself from sibling tools like dante.run_audit or the geo-focused tools.

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 by naming the content types (complaints, irony/sarcasm signals, bot anomalies, market gaps), but it provides no explicit when-to-use guidance, no exclusions, and no comparison to the sibling tools. An agent would have to infer when this tool is preferable to dante.run_audit or others.

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. It says what artifacts are generated but not whether anything is published or written externally, whether the output is returned inline, what permissions or rate limits apply, or whether generation is deterministic. For a content-generation tool with zero annotation coverage this leaves meaningful behavioral 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?

A single dense sentence that front-loads the verb and enumerates the deliverables without filler. It is efficient, though the parenthetical artifact list slightly crowds out more useful routing 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?

Seven parameters (five required), no output schema, and no annotations make this a complex tool, yet the description says nothing about how inputs shape the output or what the caller receives. It names the artifacts but not the return structure or the role of the five required inputs, leaving significant gaps for a tool of this complexity.

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 only 14% — just target_oracle is documented — and the description mentions no parameters at all. The phrase 'inaccurate AI claims' loosely gestures at hallucination_claim and verified_fact, but brand_name, domain_url, lang, and source_url_to_counter get no explanation in either place, so the description fails to compensate for the coverage gap.

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 ('Generate') and names the concrete artifacts produced (Markdown + HTML ClaimReview blocks, FAQPage JSON-LD, verification prompts) for a specific goal (remediating inaccurate AI claims). This is distinguishable from siblings like run_audit or forge_geo_shield, though it never explicitly contrasts itself with them.

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 statement of when to use this tool versus the sibling tools, no prerequisites, and no exclusions. The use case ('remediate inaccurate AI claims') is implied by the goal clause but the agent must infer the trigger conditions on its own.

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 it does disclose meaningful traits: external multi-LLM querying and SHA-256 cryptographic attestation of results. However, it never says whether the audit is read-only or persists data, how long it runs, what 'on danteix.com' implies (published report? hosted endpoint?), or what the paid-key requirement means for cost.

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 the action verb first and no filler. It is dense with brand jargon but wastes no words.

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 five-parameter, no-output-schema, no-annotation tool, the definition should say what comes back (report structure, attestation artifact) and what side effects occur. It hints at the attestation output but leaves the return shape and execution semantics underspecified.

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 all five parameters, including the vertical_mode options and the live_geo paid-key caveat, are already documented. The description adds no parameter-level meaning beyond the schema, 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 ('Execute') and a clearly scoped resource (a multi-model GEO/brand-reputation audit across ChatGPT, Gemini, Perplexity), which separates it from the sibling probe/query/forge/remediate tools. The '9-Circle Dante IX' branding is proprietary jargon an agent cannot decode, keeping it 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?

No statement of when to run a full audit versus dante.probe_geo_share or dante.query_street_friction, no prerequisites, and no note that live_geo incurs paid API usage. The agent is left to infer timing and alternatives entirely.

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