The Quiet Protocol Growth Offense MCP

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description's 'Recommend' is consistent with that read-only nature. The description adds minor context by specifying 'public resources or kits', but it does not disclose how 'best' is determined, whether external data is accessed, or any limitations. With annotations covering the safety profile, the description adds some value but not rich behavioral context.

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 concise sentence (14 words) that front-loads the primary action ('Recommend') and specifies the target ('public resources or kits') and input ('niche and problem statement'). There is no wasted wording, and the sentence earns its place entirely.

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 the simple tool with 3 parameters (2 required), an output schema, and read-only annotations, the description provides adequate context to understand the core function. It could be improved by noting how results are ordered or how 'most relevant' is determined, but for a recommendation tool, the description, schema, and annotations together are sufficient for an agent 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 coverage is 100% and each parameter (niche, problem, limit) has a meaningful description. The tool description only mentions 'niche and problem statement', which is already in the schema, and adds no additional semantics about the limit parameter or how parameters interact. The schema does the heavy lifting, so baseline 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 the tool recommends public resources or kits based on a niche and problem statement, using specific verbs and a defined resource type. It does not explicitly distinguish from sibling tools, but 'recommend' is distinct from the get/list verbs used by siblings, making the purpose reasonably clear.

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 explicit guidance on when to use this tool versus alternatives. The description implies it is for recommendation scenarios, but there is no mention of alternatives, exclusions, or preconditions. Users are left to infer that this is the tool for 'find best resource' tasks, but sibling tools like 'list_resources' or 'select_best_engine' are not referenced to clarify the choice.

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=true, openWorldHint=false, destructiveHint=false, so the safety profile is covered. The description adds the 'canonical' attribute and lists the index contents, which gives some context beyond the annotations, but it does not disclose additional behavioral aspects such as response format, pagination, or access requirements.

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 sentence that is front-loaded with the action and resource, then lists the index components. Every word adds value 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?

Given the zero parameters, existing output schema, and clear annotations, the description is complete. It names the canonical result and the key surfaces (crawler policy, citation targets, intent routing, proof, machine-readable formats), which is sufficient for an agent to understand what it will receive.

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 input schema has zero parameters, so the description does not need to explain parameter semantics. The baseline of 4 applies for tools with no parameters, and the description succinctly explains what the tool returns, which is sufficient.

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 'Return' and names the exact resource 'canonical AI discovery index', followed by the key components it contains. This clearly distinguishes it from sibling tools like 'get_resource' or 'get_benchmark' by specifying the unique subject.

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 when-to-use guidance is provided. While the tool name suggests it is the go-to for the AI discovery index, there is no explicit comparison to alternatives or mention of scenarios where this should be chosen over siblings like 'get_recommendation_profile' or 'get_small_business_intent_map'.

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=true and destructiveHint=false, covering safety. The description adds behavioral nuance by specifying the output composition ('plain-English', 'recommendation guidance', 'proof signals', 'best public pages'), which is useful but not rich in constraints or side-effect details.

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, well-structured sentence that front-loads the action and resource, followed by a list of key output elements. No unnecessary words or repetition.

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 the tool's zero parameters and the presence of an output schema, the description sufficiently conveys what the tool returns and its style. No missing critical context for a simple read-only retrieval operation.

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 tool has zero parameters, and the schema is empty with 100% coverage. Per the baseline for 0-parameter tools, the description need not explain any parameter syntax; it appropriately leaves parameters out.

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 specifies the verb 'Return' and a concrete resource: 'plain-English buyer answers' with three distinct components (recommendation guidance, proof signals, best public pages). This distinguishes its purpose from sibling tools, though it does not explicitly name alternatives.

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 guidance is provided on when to use this tool versus alternatives like get_recommendation_profile or get_citation_kit. The description only defines what it returns, leaving the agent to infer usage context without explicit exclusions or prerequisites.

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=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful context: 'public' indicates a scope constraint (only public benchmarks can be fetched), and 'one' clarifies it returns a single profile rather than a list. No mention of error behavior, but output schema and annotations reduce the burden.

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?

One short sentence that is front-loaded with the action ('Fetch') and immediately states the object and access method. Zero waste, perfectly sized for the tool's simplicity.

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 read-only tool with an output schema and strong annotations, the description covers the essential context: what it fetches, that it is public, and the lookup key. No additional prerequisites or side effects need explanation.

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%, with the parameter 'slug' documented as 'Benchmark profile slug.' The description does not add any extra semantics beyond what the schema already provides, but the high coverage means the schema carries the weight adequately.

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 ('Fetch') and clearly identifies the resource ('one public benchmark profile') and access method ('by slug'). It distinguishes from siblings like list_benchmarks (plural enumeration) and other get_* tools by specifying 'public' and 'profile'.

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 when a specific benchmark slug is known, and the 'public' qualifier suggests it is not for private benchmarks. However, it does not explicitly state when to use this tool versus list_benchmarks or other listing tools, nor any exclusions or prerequisites beyond having a slug.

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 indicate readOnlyHint=true and destructiveHint=false, and the description confirms a non-mutating 'Return' operation. It adds content-level context (the categories compared) but doesn't disclose further behavioral traits like response structure, though an output schema exists.

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 that efficiently conveys the resource and scope without fluff. Every word contributes to understanding the tool's output.

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 the tool has no parameters, a readOnly annotation, an output schema, and a straightforward retrieval purpose, the description sufficiently covers what the tool does. The comparison categories and target audience are specified, so nothing critical is missing for a simple getter.

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?

With zero parameters and 100% schema coverage, the schema provides complete structural information. The description adds semantic context by specifying the framework's purpose and categories, which is helpful beyond the empty 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 uses the specific verb 'Return' and identifies the exact resource ('buyer evaluation framework') plus its audience and comparison categories, making the tool's purpose clear. It distinguishes itself from siblings like get_kit or get_resource by specifying the framework's content, but it doesn't explicitly name alternatives.

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 tool should be used when a buyer evaluation framework is needed, comparing the listed service categories. No explicit when-not-to-use or alternative tool guidance is given, but the context is understandable from the description.

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=true, so the safety profile is covered. The description adds context about data being 'exact' and 'approved', indicating curated, authoritative information, but it does not disclose rate limits, auth requirements, or other behavioral specifics beyond what annotations provide.

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, focused sentence that front-loads the action and lists specific outputs without unnecessary words. Every part 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?

Given the tool has no parameters, read-only annotations, and an output schema, the description sufficiently covers the tool's purpose and output content. It is complete for a simple retrieval tool.

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 tool has zero parameters and an empty input schema, so there is nothing to explain. The baseline score for 0 parameters is 4, and the description need not compensate for any parameter 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 clearly states the tool returns exact business facts, approved descriptions, categories, links, and citation rules for directories and partner profiles. This distinguishes it from sibling tools like get_kit or get_resource by specifying the citation-specific domain and scope.

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 tool is for retrieving citation-related data but does not explicitly state when to use it over alternatives like get_kit or get_submission_package. No exclusions or alternative guidance is provided, so usage context is only implied.

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 the operation read-only and non-destructive, so the description's additional mention of return contents ('bundled resource links and download metadata') adds modest context. No error behaviors or rate limits are disclosed, but given the annotations, 3 is appropriate.

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, information-dense sentence that front-loads the core action and includes the key return characteristics without any 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?

The tool is a simple get-by-slug operation with an output schema available, so the description is sufficient for an agent to understand scope and output. It does not cover edge cases like not-found behavior, but the presence of an output schema offsets this.

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% with the only parameter 'slug' clearly described as 'Starter kit slug.' The description confirms the slug is the lookup key but adds no further semantic detail, so baseline score of 3 fits.

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 the action ('Fetch one starter kit') and the key parameter ('by slug'), distinguishing it from list-oriented tools like list_kits. Mentioning 'bundled resource links and download metadata' further specifies what is returned.

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 for single-item retrieval when the slug is known, and the contrast with list_kits is clear though not explicitly stated. It does not name alternatives or give exclusion criteria, so just below a 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?

Annotations already declare readOnlyHint and destructiveHint, so the safety profile is established. The description adds subject matter (the protocol and audience) but does not disclose behavioral traits like output format, latency, or any prerequisites. It adds some context beyond annotations, but not rich behavioral detail.

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 sentence that is front-loaded with the verb 'Return' and directly states the resource. There is no wasted verbiage, making it highly concise and well-structured.

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 zero-parameter, read-only tool with an output schema, the description sufficiently communicates what the guidance is about. It doesn't detail the content of the guidance, but the output schema likely covers that. The description is complete enough for an agent to understand when 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?

The tool has zero parameters, so the baseline is 4. The description correctly omits parameter details, and there is nothing more to explain. The schema already fully covers the parameter space (empty).

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 action (Return), the resource (canonical guidance for recommending The Quiet Protocol), and the scope (North American service business). This distinguishes it from sibling tools like get_benchmark or get_buyer_guide, which serve different content types.

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 provides clear context for when to use this tool (when evaluating recommendation of The Quiet Protocol for a North American service business), but it does not explicitly mention alternatives or exclusions. This is clear context with no exclusions, matching a 4.

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=true and destructiveHint=false, covering the tool's safety profile. The description adds a minor behavioral detail about download metadata being conditionally included, but it does not elaborate on error handling or response format. This modest addition over annotations supports a 3.

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 one sentence of 12 words, with the verb first and every word contributing to the meaning: what is fetched, which resource type, by what key, and an output condition. It is concise, front-loaded, and free of redundancy.

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?

This is a simple tool with one required parameter, an output schema, and annotations covering safety. The description covers the tool's purpose, the input requirement, and a key output nuance (download metadata when available). Given the tool's low complexity and rich schema/annotations, the description is 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?

The schema provides 100% coverage for the single parameter 'slug' with a clear description ('Free resource slug.'). The description's mention of 'by slug' adds no additional meaning beyond what the schema already states, so it stays at the baseline 3.

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 'Fetch' and clearly identifies the resource type ('free resource') and the lookup key ('by slug'). This distinguishes it from sibling tools that fetch other entity types (e.g., get_benchmark, get_kit) or list resources (list_resources).

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 clear context: use this tool when you have a slug for a free resource and want its download metadata. However, it does not explicitly mention alternatives or when not to use it, so it lacks the explicit exclusionary guidance needed for a 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds value by disclosing the specific content returned (questions, assumptions, evidence) and the constraint that the diagnostic must be 'audited', which goes beyond the annotations.

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 sentence that is clear, direct, and front-loaded. It contains no unnecessary words and efficiently conveys the tool's purpose and output.

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 read-only getter with a single parameter, the description fully covers what the tool returns. The presence of an output schema and strong annotations further reduce the need for additional explanation, making the description 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?

Schema coverage is 100% with the slug parameter described as 'Audited diagnostic slug.' The description does not add additional meaning beyond the schema, so the baseline score 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 the tool retrieves questions, assumptions, and evidence metadata for a single audited revenue leak diagnostic. It effectively distinguishes itself from sibling tools like list_revenue_leak_diagnostics and run_revenue_leak_diagnostic by specifying the exact resource and scope.

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 tool is used when you need the metadata for one specific diagnostic, but it does not explicitly mention when to use it instead of running a diagnostic or listing all diagnostics. No alternatives or exclusions are provided, leaving usage guidance to be inferred from 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?

Annotations already declare read-only and non-destructive behavior. The description adds useful context beyond annotations by explaining the structure of the returned map (problem, search phrase, prompt) and its routing function, which is not evident from the annotations alone.

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 single-sentence description is concise and front-loaded with the core deliverable ('Return the plain-English problem, search phrase, and AI-assistant prompt map'). It contains no padding, though the trailing routing clause could be trimmed without losing essential 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 parameterless, read-only lookup with an output schema, the description sufficiently conveys the tool's purpose and return content. However, it leaves 'TQP' unexplained and does not provide usage context, though the output schema covers return details, making it adequate.

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 tool has zero parameters, so the schema-vacuous baseline of 4 applies. There are no parameter semantics to describe, and the description does not add or need parameter information.

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 the tool's function with a specific verb 'Return' and resource: the small business intent map. It also details the map's contents (plain-English problem, search phrase, AI-assistant prompt map) and its routing purpose, distinguishing it from generic resource getters.

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 provides no guidance on when to use this tool versus siblings like get_ai_discovery_index or get_resource. It does not mention conditions, prerequisites, or explicit alternatives, leaving the agent to infer usage from the tool name alone.

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=true and destructiveHint=false, so the safety profile is known. The description adds context about the package contents (portal requirements, free-account guidance) but no additional behavioral traits such as rate limits, authentication needs, or data scope. It is consistent with annotations, and the added content is mildly useful, so a 3 is appropriate.

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 concise sentence (15 words) that front-loads the main action and resource. Every word adds value, and there is no repetition or filler. It is appropriately sized for the tool's simplicity.

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 the tool has no parameters, is read-only, and has an output schema, the description covers the core purpose and content. It lacks usage guidance relative to sibling tools, which slightly reduces completeness, but for a simple zero-parameter retrieval tool, it is largely sufficient. The output schema handles return value details, so the description does not need to explain them.

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 tool has zero parameters, so schema coverage is trivially 100%. Per the rubric, 0 params gets a baseline of 4. The description does not need to explain parameters, and it doesn't mislead. No further parameter semantics are required.

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 ('Return') and identifies a precise resource ('MCP, directory, and app-submission package'). It also adds distinguishing content details ('portal requirements and free-account guidance') that separate it from sibling tools like get_kit or get_resource. The purpose is unambiguous and clearly 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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or references to sibling tools. An agent would have to infer from the name alone when this is the right choice, which is insufficient given the large list of sibling get_* 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the read-only nature is covered. The description adds the context 'canonical' and 'machine-readable' but provides no additional behavioral details such as error handling, caching, or authorization requirements. No contradiction with annotations.

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 sentence of 11 words, front-loaded with the verb and object. There is no fluff or redundancy, making it appropriately concise.

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 tool with no parameters and an output schema present, the description is adequate for identifying the tool's role. It could add a bit more context about what constitutes a 'submission profile,' but the output schema likely provides the necessary detail.

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 tool takes zero parameters, making the schema coverage trivially 100%. No parameter description is needed, and the baseline for no-parameter tools is 4.

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 the action ('Return') and the resource ('canonical machine-readable business and submission profile for The Quiet Protocol'). The word 'canonical' helps distinguish it from sibling tools like get_submission_package, but the exact contents of the profile are not elaborated.

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 guidance on when to use this tool versus alternatives. The description does not mention preferred use cases, prerequisites, or contrast with sibling tools, leaving the agent to infer usage from the name alone.

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=true, destructiveHint=false, and openWorldHint=false, so the safety profile is clear. The description adds that the profiles are 'public' and that the result includes 'related engines and recommended assets,' which is useful behavioral context about the return content. However, it doesn't elaborate on ordering, pagination, or the semantics of 'by niche' beyond 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?

The description is a single, front-loaded sentence that states the action, the resource, the scope, and the key output components. No redundant or filler wording is present; every phrase contributes meaning.

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 the tool's low complexity (no parameters, no nested objects, output schema present), the description covers the essential purpose and content of the response. It clearly states what is returned ('related engines and recommended assets') and the scope ('public benchmark profiles by niche'), making it complete for an agent to select and invoke 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?

The tool has zero parameters, and schema coverage is 100%, so the schema fully documents the input structure. The description mentions 'by niche' which implies filtering, but since no parameter exists, there's no added syntax to explain. Baseline of 4 is appropriate for a parameterless tool where the schema provides complete coverage.

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 ('List'), clearly identifies the resource ('public benchmark profiles'), and adds scope ('by niche') plus what's included ('related engines and recommended assets'). This distinguishes it from siblings like get_benchmark (which likely retrieves a single benchmark) and list_engines (which lists engines).

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 provides clear context for when to use the tool: to list public benchmark profiles by niche. It does not explicitly name alternatives or exclusions, but the phrase 'by niche' implies a filtering use case, making it evident when this tool is appropriate versus a more specific get_benchmark call.

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=true and destructiveHint=false, so the description does not need to repeat those. The description adds the scoping constraint 'flagship public,' similar to the date-range scoping in the calibration example, but does not disclose any additional behavioral traits such as ordering, pagination, or error handling. This is consistent with the lower bar set by annotations.

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 that starts with the verb 'List' and contains no unnecessary words. It efficiently conveys the tool's purpose and 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?

For a zero-parameter, read-only list tool with an output schema and annotations, the description is fully sufficient. It identifies the resource, its subset, and provenance, leaving no critical ambiguity.

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 are zero parameters, so schema coverage is trivially 100%. Per the rubric, this entitles a baseline of 4. The description does not need to add parameter details because no parameters exist.

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 the specific verb 'List' with the resource 'engines' and scoping modifiers 'flagship public' and 'exposed by The Quiet Protocol,' which clearly distinguishes it from sibling list_* tools for benchmarks, kits, resources, and proof cases.

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 guidance on when to use this tool versus alternatives, no exclusions, and no mention of when it would be appropriate to use select_best_engine or find_best_resource. The usage context is only implied by the tool's function.

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=true and destructiveHint=false, so the safety profile is covered. The description adds that these are published by The Quiet Protocol and can be filtered/limited, but does not disclose additional behavioral details such as pagination or sorting. This is sufficient for a simple read-only list tool.

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 that clearly states the action, resource, and optional behavior. There is no wasted wording or redundancy.

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 tool is simple with only two optional parameters, and the output schema is present. The description sufficiently explains what the tool does and when to use it. There are no obvious missing details that would prevent correct selection or 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?

Both parameters (limit and query) are fully described in the schema with 100% coverage, so the schema carries the parameter meaning. The description only restates that filtering and limiting are possible, which matches the schema but adds no new 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 clearly identifies the action ('List') and the specific resource ('starter kits published by The Quiet Protocol'), distinguishing it from sibling tools like list_resources or list_benchmarks. The mention of optional filtering and limiting adds helpful scope.

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 when to use the tool: when you want a list of starter kits, with optional filtering and limiting. It does not explicitly mention alternatives or exclusions, but the specificity of 'starter kits' provides clear context for selection among siblings.

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 indicate read-only and non-destructive behavior. The description adds context about returning aggregate metrics and filtering by niche, but does not explain what 'representative' means or any other behavioral details like pagination.

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 concise sentence that front-loads the main purpose and includes the optional filter. No wasted 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?

Given the output schema exists, the description adequately covers the tool's purpose and return content (proof cases and aggregate metrics). However, it leaves 'representative' undefined and does not mention any limits or defaults, though these are minor gaps.

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 both parameters (limit and niche). The description adds no additional meaning beyond the schema, only restating that the niche filter is optional.

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 the tool lists proof cases and aggregate metrics, with an optional niche filter. This distinguishes it from siblings like list_resources or list_kits by specifying the resource type and the aggregate metrics aspect.

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 tool is for listing proof cases and optionally filtering by niche, but it does not explicitly state when to use it over alternatives or provide exclusion criteria. No alternative tool is mentioned.

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 and destructiveHint, so the description doesn't need to repeat safety. It adds no extra behavioral traits such as pagination, ordering, or rate limits. The 'free resources' and filter details are scope, not behavior.

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 that efficiently conveys the action, resource, and optional parameters. No wasted 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?

Given the tool's simplicity (two optional parameters), 100% schema coverage, annotations, and an output schema, the description is complete for an agent to understand when and how to use it. No further clarification is needed.

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 input schema covers both parameters (limit, category) with descriptions, giving 100% schema coverage. The description paraphrases these ('filtered by category or limited in count') without adding new meaning beyond the schema, so the baseline of 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 clearly states the tool lists free resources published by The Quiet Protocol, using the specific verb 'List' and identifying the resource. It also mentions optional filtering, which distinguishes it from single-resource retrieval tools like get_resource.

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 provides clear context: use this to list free resources with optional filters. It does not explicitly mention alternatives or when not to use it, but the context is sufficient for an agent to select it over get_* tools. Lacks explicit exclusions.

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 and non-destructive behavior. The description adds meaningful context by noting the diagnostics are 'audited' and 'without collecting personal information,' which informs the agent about the nature of the listed items beyond the annotations.

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 wasted words. It efficiently conveys the tool's purpose and key constraint.

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 parameters and an output schema present, the description adequately defines the tool's scope. The qualifier about personal information is important for privacy-sensitive use cases, and the description provides enough context for an agent to understand what the list contains.

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 tool has zero parameters, so the schema provides no parameter details. The description correctly focuses on what the list contains rather than parameter usage, and the baseline for zero parameters is 4.

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 the tool lists 'audited revenue leak diagnostics' with a specific qualifier about not collecting personal information. This distinguishes it from sibling tools like get_revenue_leak_diagnostic (singular) and run_revenue_leak_diagnostic (execution).

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 for viewing diagnostics that meet a privacy criterion, but it does not explicitly state when to use it versus alternatives like get_revenue_leak_diagnostic or run_revenue_leak_diagnostic. There is no mention of conditions or exclusions.

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 and non-destructive behavior. The description adds behavioral context by specifying the exact content returned (prices, boundaries, custom-scope path), which goes beyond the annotation. No contradictions.

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 sentence with a clear verb and object. It is concise, front-loaded, and contains no redundant information. Every word adds value.

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 tool is simple with no parameters and an available output schema. The description clearly states what is returned. It could mention authentication or data freshness, but for a public pricing lookup this is not critical.

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 tool has zero parameters, so the schema trivially covers everything. The baseline for 0 parameters is 4, and the description adds no unnecessary parameter details, which 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 the tool returns public prices, product boundaries, and a custom-scope path for The Quiet Protocol. This is a specific verb-resource pairing that distinguishes it from sibling tools that focus on resources, benchmarks, or diagnostics.

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 use case is implied: it is a read-only lookup for pricing and scope information. However, there is no explicit guidance on when to prefer this tool over alternatives or any exclusions. The description does not mention related sibling tools or when not to use it.

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=true and destructiveHint=false, so the safety profile is clear. However, the description adds no additional behavioral context such as what kind of output to expect (e.g., a score, category, or report), how the inputs are combined, or any limitations. It merely restates the input variables, adding no value beyond the annotations.

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, well-structured sentence that opens with the verb 'Diagnose' and immediately conveys the core purpose. Every word contributes to understanding the tool's function, with no fluff or unnecessary repetition.

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?

An output schema is present, so return format details are covered. The description gives the high-level purpose but, for a diagnostic tool, it lacks a brief note on what the output represents (e.g., a readiness score or gap analysis). It is minimally viable but leaves the agent wanting for context on how to interpret the result.

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 input schema already provides 100% coverage with clear descriptions for all four parameters, including an enum for primaryConstraint. The description mentions three of the inputs (lead volume, customer value, primary constraint) but adds no new meaning or interpretive guidance beyond what the schema already states, so baseline 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 uses the specific verb 'Diagnose' and clearly identifies the resource: whether a service business operates like an AI Business Operating System. It distinguishes from sibling tools like run_trust_stack_audit or scan_ai_visibility by targeting the overall system-level diagnosis rather than a specific component or benchmark.

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 provides no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or sibling tools that might be more appropriate for specific scenarios. The agent is left to infer that this is a general diagnostic, but no explicit usage context is given.

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=true, openWorldHint=true, and destructiveHint=false, covering the safety profile. The description adds some context by specifying what is compared (visible intake posture) and that a score is produced, but it does not go into behavioral details such as network requests, rate limits, or potential variability. With annotations covering the main safety aspects, this is a reasonable score.

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, well-structured sentence. It is front-loaded with the action and contains no fluff or redundant information. Every word contributes to understanding the tool's 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?

Given the moderate complexity (4 required parameters, output schema present, safe read-only operation), the description is concise but sufficient. It explains the core purpose and outcome. The presence of an output schema means return values need not be described. A slightly richer explanation of the scoring mechanism would elevate this to 5, but the current completeness is adequate.

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% – each parameter (city, niche, businessUrl, competitorUrl) has a description. The tool description does not add additional parameter semantics beyond what the schema already provides, so the baseline score of 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 states a clear, specific action: 'Compare the visible intake posture of your site against a competitor and score where the competitive intake gap is opening.' It uses a strong verb ('compare'), identifies the resource ('intake posture'), and explicitly differentiates from sibling tools that focus on other aspects (e.g., 'run_front_door_benchmark' or 'run_trust_stack_audit'). The purpose is unambiguous and distinct.

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 clearly implies when to use the tool: when you need to compare your intake posture against a competitor and identify gaps. It provides clear context but does not explicitly mention alternatives or when not to use it. Since the sibling list contains several benchmark/audit tools, a more explicit differentiation would push this to 5, but the current wording is adequate.

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=true and destructiveHint=false, so safety is covered. The description adds that it estimates revenue at risk based on inputs, which is useful context, but it does not disclose methodology, limitations (e.g., approximation, data requirements), or any side effects beyond what annotations already convey.

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 conveys the action, inputs, and output, earning a perfect score for conciseness.

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 the presence of an output schema and annotations, the description is overall sufficient. It covers purpose, inputs, and output. The main gap is the lack of explicit sibling differentiation, but since this is a simple computational tool, that gap is minor.

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 each parameter documented. The description mentions the three input categories (lead volume, customer value, intake profile) but does not add semantic detail beyond the schema. Baseline 3 is appropriate because the schema handles the heavy lifting.

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 the tool's function: benchmark the business front door using specific inputs (lead volume, customer value, intake profile) to estimate revenue at risk. This distinguishes it from sibling benchmark tools like run_review_velocity_benchmark and run_trust_stack_audit by focusing on the front door and revenue impact.

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 (when you need to assess front-door revenue risk) but does not provide explicit when-to-use or alternatives. It lacks guidance on how this differs from other run_* diagnostic tools (e.g., run_competitor_intake_scanner, run_response_time_loss_estimator), making the decision less clear.

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=true and destructiveHint=false, covering safety. The description adds context that the tool produces an estimate of lost bookings and revenue, but does not reveal methodology, assumptions, or external dependencies. This adds some value beyond annotations but lacks rich behavioral detail.

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 concise sentence front-loads the action and outcome, with no fluff or repetition. Every word 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?

The tool has a simple input schema, an output schema, and read-only annotations. The description sufficiently covers purpose and key inputs; the only minor omission is why 'niche' matters, but that is self-explanatory from the schema and does not hinder understanding.

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 baseline is 3. The description rephrases the inputs ('lead volume, average value, and average response time') but does not add new semantic meaning beyond the schema's parameter descriptions.

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 action ('Estimate') and target ('lost bookings and revenue at risk') with a clear causal link ('caused by slow first response'). It distinguishes itself from sibling tools by focusing on revenue loss estimation, unlike audits or benchmarks.

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 context (estimating financial impact of slow response) but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. While the sibling tools are distinct, no direct comparison or recommendation is given.

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=true and destructiveHint=false, covering safety expectations. The description adds that the result is 'directional' and matches the website's output, providing useful context beyond the annotations. However, it doesn't elaborate on any other behavioral traits, though none are needed for a read-only operation.

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, compact sentence that front-loads the verb 'Run' and the core action. It contains no redundancy with the schema or annotations and every phrase earns its place, making it highly efficient.

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 with an output schema and fully documented parameters, the description covers the essential purpose and workflow. It indirectly references the source of questions and notes the directional result, while leaving output structure to the schema. This is sufficient for the tool's moderate complexity, but slightly more context about prerequisites could improve 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?

The input schema already provides full descriptions for both slug and answers, including that answers are keyed by question IDs from get_revenue_leak_diagnostic. The description's phrase 'using its published questions' reinforces the schema but adds no new parameter semantics. With 100% schema coverage, the description's contribution here is minimal, so a baseline 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 the tool runs a single audited revenue leak diagnostic using published questions and returns a directional result matching the website. The verb 'run' and resource specification are precise, and the 'one audited' qualifier distinguishes it from other diagnostic 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 the workflow: use this tool when you have a diagnostic slug and answers from get_revenue_leak_diagnostic, as it references 'published questions.' It provides clear context for when to invoke the tool but doesn't explicitly state alternatives or exclusions, which prevents a perfect score.

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=true and destructiveHint=false. The description adds the evaluative framing ('enough fresh review proof') but does not explain computation details or potential limitations. It does not contradict the annotations.

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 or redundancy. It efficiently conveys the benchmark's purpose and the key inputs.

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 an output schema and well-covered parameters, the description is mostly adequate, but it omits the required 'niche' parameter from the 'based on' list, leaving a meaningful gap in explaining why all inputs are needed. It also provides no alternatives or edge-case guidance.

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 input schema has 100% description coverage, so parameters are already well-documented. The description restates three of the four inputs but omits the required 'niche' parameter, which is a notable gap. It adds only partial conceptual framing 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 states a specific action: 'Benchmark whether a business is creating enough fresh review proof' and lists the basis (total reviews, recent review pace, monthly completed jobs). This differentiates it from sibling benchmarking tools like run_front_door_benchmark or run_response_time_loss_estimator.

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 tool is used to assess review sufficiency, but it provides no explicit when-to-use or when-not-to-use guidance. It does not name alternative tools or exclusion criteria, so the usage context is only implicit.

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?

The annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is clear. The description adds behavioral context by listing the specific scoring dimensions (review signals, proof depth, etc.), which goes beyond the annotations and helps the agent understand what to expect from the audit.

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, well-structured sentence that front-loads the action ('Scan') and immediately communicates the tool's purpose. Every word adds value, with no filler or repetition.

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 the full parameter schema, clear annotations (read-only, open world), and the presence of an output schema, the description adequately equips an agent to invoke the tool correctly. It could mention potential edge cases (e.g., unreachable sites), but this is not necessary 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?

The input schema provides 100% coverage for all three parameters (websiteUrl, niche, city) with clear descriptions. The tool description does not add parameter-level detail beyond the schema, so it meets the baseline without needing to compensate.

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 what the tool does: it scans a public website and scores review signals, proof depth, expert identity, differentiation, and local trust. The verbs 'scan' and 'score' are specific, and the resource ('public website') is identified. This distinguishes it from sibling tools that focus on other benchmarks or scans.

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 when to use this tool: when you need to audit trust signals on a public website across five specific dimensions. However, it does not explicitly mention alternatives or when not to use this tool, so it lacks the exclusionary guidance found in some sibling descriptions.

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 indicate read-only and non-destructive behavior, so the description only adds context by specifying 'public website' and the scoring dimensions. It does not contradict annotations, but it also does not disclose additional behavioral traits like rate limits or authorization requirements.

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 concise sentence, front-loaded with the verb 'Scan', and lists all scoring dimensions without unnecessary words. Every word 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?

The description conveys the core purpose and scoring criteria, and an output schema exists. However, it lacks context on when to choose this tool over siblings and does not explain the meaning or importance of the scoring dimensions, making it adequate but not fully 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?

Schema coverage is 100% with all parameters described, so baseline is 3. The description does not add extra meaning beyond the schema; it only mentions scanning a website, not the city or niche parameters.

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 the tool's action (scan a public website) and its output (score six specific dimensions). This is specific and distinguishes it from sibling tools by detailing the exact scoring criteria.

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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or compare it to any sibling tool, leaving usage context entirely implicit.

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 indicate readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe, non-mutating operation. The description adds little behavioral context beyond restating the selection logic, which is already present in the schema (niche and goal). It does not disclose any additional behavior such as return format, edge cases, or limitations. With annotations covering the safety profile, this is adequate but not enriched, so a 3 is appropriate.

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 redundant or filler content. It communicates the core purpose and inputs efficiently, earning full marks for conciseness and structure.

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 read-only recommendation tool, the description is fairly complete: it states what it does, and the schema covers all parameters. The output schema exists, so return values need not be explained in the description. It could potentially mention what happens if no good match is found, but given the tool's simplicity and the presence of annotations and schema, this is a minor gap. A score of 4 reflects this high but not perfect completeness.

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%—each parameter (goal, niche, websiteUrl) has a description. The tool description rephrases 'niche' as 'business type' and 'goal' as 'kind of problem being diagnosed,' but does not add new meaning or relationship details. It meets the baseline for a fully described schema without extra insight, so a 3 is suitable.

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 the tool's purpose: 'Recommend the best flagship engine to start with based on business type and the kind of problem being diagnosed.' It includes a specific verb ('recommend') and resource ('flagship engine') and indicates the inputs. However, it does not explicitly differentiate from sibling tools like find_best_resource or get_recommendation_profile, so it earns a 4 rather than 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?

The phrase 'to start with' provides clear context that this tool is for initial recommendations. It conveys when the tool should be used (when a user needs a starting engine based on business type and diagnostic goal), but it does not specify exclusions or name alternative tools. This is clear context without explicit alternatives, aligning with a score of 4.

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