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 audit outcome and remediation code generation, but it does not clarify that remediation is controlled by the remediate parameter or mention side effects like network access to the requested site.

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 entire description is one tight sentence that leads with the core action, includes key measurement details in parentheses, and avoids any filler. Every phrase 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 tool without an output schema, this description explains what the result is (score across five pillars) and what remediation can result. It does not thoroughly address the optional remediate flag, but the parameter schema documents that, making the definition sufficiently complete.

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?

Input schema describes all three parameters (url, name, remediate) in detail, so schema coverage is 100%. The description adds domain context but no additional parameter-level semantics, which matches the baseline expectation.

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?

Description uses a specific verb ('Audit') and identifies the exact resource ('website's Agentic Commerce Readiness'), defines the /100 scoring across five pillars, and mentions remediation code output. This strongly differentiates it from sibling tools like geo_audit and geo_social_generate.

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 clearbly scopes usage to auditing agentic commerce readiness, giving an agent strong context. However, it doesn't explicitly mention when to use alternatives or state exclusions, though the sibling names suggest different domains.

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 does disclose the analysis dimensions (intent gaps, market white spaces) and the generated artifacts (BLUF blocks, Schema.org markup). However, it does not state whether the tool is read-only or mutates anything, whether it makes external network calls, or what side effects a full audit (including competitor and live-signal scans) may have.

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 with no filler. The purpose is front-loaded in the first sentence and the analysis/generation scope in the second. It is efficient, though it crams several specialized terms (BLUF, RAG-first, intent gaps, market white spaces) together without any elaboration or examples.

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 complex tool with five parameters but no output schema and no annotations, the description covers the high-level analysis and generation scope. Still, it relies on unexplained industry jargon (BLUF, RAG-first, intent gaps, market white spaces) and does not indicate what a returned audit report looks like or how live_signals/competitors affect processing — gaps the missing output schema and annotations would otherwise relieve.

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 schema already documents all five parameters, including defaults and semantics. The description adds overall context (what 'rewrite' generates: BLUF + Schema.org) and clarifies purpose, but it does not add per-parameter detail beyond the schema. Baseline 3 is appropriate since 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 states specific verb ('Audit') and resource ('website visibility in generative search engines'), naming the exact engines (ChatGPT, Perplexity, Gemini, Claude). It also conveys the distinct output (RAG-first BLUF answer blocks + Schema.org markup), which clearly differentiates it from its siblings_social_generate and agentic_scan.

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 its use case (auditing generative-search visibility and identifying intent gapswhite spaces) and names its scope precisely, allowing an agent to infer when it applies. However, it never explicitly states when NOT to use it or points to a sibling alternative (e.g., when generation is needed use geo_social_generate). No exclusions are given, so selection relies on 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 are absent, so the description carries the full burden. It vaguely mentions 'mathematically aligned with market intent centroids' and 'RAG citation consensus', but does not disclose what the tool actually does beyond generating text, whether it modifies any state, requires permissions, has rate limits, or what the return format looks like. For a generation tool, 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?

A single sentence that is not excessively long but is dense with jargon ('market intent centroids', 'RAG citation consensus') that may obscure the intended meaning. The core action is front-loaded, but the added phrases provide little value to an agent unfamiliar with the domain. It is usable but not optimally clear.

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 annotations and no output schema, the description alone must make the tool self-explanatory. It lacks guidance on when to use it relative to siblings, what inputs are expected to achieve, and what kind of output to expect. The jargon suggests a sophisticated workflow but leaves too much to inference for an agent to use it correctly without additional context.

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 schema fully documents url, format, and seed_topic. The description adds no additional meaning or nuance to the parameters; it does not explain how 'url' feeds into generation or elaborate on 'seed_topic'. Baseline of 3 applies because the schema already covers all 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?

States a specific verb ('Generate') and resource ('viral social publications') and enumerates the formats (LinkedIn, X/Threads, Debate, Carousel). The phrase 'mathematically aligned with market intent centroids to build external RAG citation consensus' hints at the underlying approach, though it is jargon-heavy. It is distinct from siblings geo_audit and agentic_scan but does not explicitly differentiate, so not 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?

Provides no guidance on when to use this tool versus siblings, no prerequisites, and no exclusions. The description describes what it does but not the context in which it should be invoked or how it relates to geo_audit or agentic_scan. An agent is left to infer appropriate usage from the purpose alone.

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