Laguna Pools

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

With no annotations, the description carries full burden. It doesn't disclose side effects (if any), return format, or prerequisites beyond 'model'. It does mention delivery calculation, but lacks explicit read-only confirmation, whether it produces a breakdown, or any required parameters beyond what's obvious.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, front-loaded with the primary purpose, and no redundant information. Every word contributes to understanding what the tool does.

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 7-parameter tool with no output schema or annotations, the description is adequate but incomplete: it doesn't mention the include_furniture parameter, doesn't specify the return value (e.g., total price, breakdown), and lacks any guidance on how to interpret 'под ключ'. The base purpose is clear, but these gaps could cause an agent to overlook options or misjudge outputs.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by mapping the listed kit components (filtration, cover, border, delivery) to the boolean include_* parameters and the city parameter, making the relationship between description and schema clearer.

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 calculates the cost of a turnkey kit (pool + filtration + cover + border + delivery). The verb 'расчёт' and the resource 'комплект' are specific, and the component list distinguishes it from sibling tools like get_pool_price or get_filters.

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 bundled quote calculations, but does not explicitly state when to use this vs alternatives like get_pool_price or get_accessories. No exclusions or alternative tool names are mentioned, though the component list hints at the intended scenario.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors: fallback from knowledge base to human specialist, ticket number format (letter+2 digits), async draft preparation, and email notification to specialist. It also mentions filtering against off-target requests, injections, and profanity. This is meaningful behavioral context beyond what a typical tool description would include.

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 paragraph but information-dense, covering purpose, pool types, flow, topics, and security filters. It is not excessively long, and every sentence contributes meaningful detail. However, it lacks visual structure (bullets or sections), making it slightly harder to scan quickly. Still, it earns its place overall.

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 8 parameters, 0 required, and no output schema, the description provides a solid overview of the consultation process, fallback behavior, and topic scope. It does not explain return values or error handling, but the flow description gives enough context for an agent to understand expected behavior. The mode semantics are left to the schema, which is adequate. Slight gap: no explicit guidance on when each mode is appropriate, but this is partially covered by the schema descriptions.

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 has 100% coverage with descriptions for all 8 parameters, so the baseline is 3. The tool description adds value by specifying the exact ticket number format (letter+2 digits, e.g., А05) and elaborating on pool types and modes within the flow context. This extra information helps clarify how parameters relate to the overall process.

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 it provides service consultation for pool maintenance across multiple pool types and lists specific topics. The verb 'consult' and resource 'pool maintenance' make the purpose explicit. However, it does not explicitly differentiate from sibling tools like 'tech_consult', relying instead on topic enumeration to imply 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 gives a clear flow (knowledge base → miss → ticket with number → AI draft → specialist email) and lists covered topics, which implies when to use the tool. It also mentions protection against off-target requests, suggesting the tool is for pool-related inquiries only. However, it does not explicitly state alternatives or conditions for using sibling tools, so usage guidance remains implicit rather than explicit.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether this is a read-only operation, what the response contains (e.g., dealer names, addresses, contact info), or any limitations beyond the number of cities. The lack of output schema makes this omission significantly detrimental.

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 exactly two short sentences, front-loaded with the action verb and critical object ('Найти дилера'), followed by a useful scope detail ('175 городов России'). Every word earns its place, with no repetition or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema and no annotations, so the description must provide sufficient context about expected results and parameter handling. It does not explain what data is returned (e.g., dealer list, map link) or whether both parameters are optional and how they combine. For an agent to invoke this tool correctly, more behavioral info 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 already provides descriptions for both parameters (city and region), covering 100% of the semantics. The tool description adds only that the search is 'in the city', but does not clarify the relationship or precedence between city and region. Per the calibration, baseline 3 is correct since the schema does the heavy lifting, and the description adds minimal value.

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 'Найти' (find) with a clear resource ('дилера Laguna Pools') and geographic scope ('в городе', '175 городов России'). It unambiguously differentiates this tool from all sibling tools, none of which relate to finding dealers.

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 finding dealers, but it does not explicitly state when to use it versus alternatives or mention any exclusions. Since no sibling tool has similar functionality, the usage is self-evident but not explicitly guided, fitting the 'implied usage' category.

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 bears the full burden of behavioral disclosure. It only states that a proposal is generated and lists sections, but does not reveal side effects, return format, required permissions, or whether the proposal is simply returned or persisted. This is a significant gap for a tool with no annotation support.

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 provides a useful enumeration of the 12 sections. No filler or redundant content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (nested extras, multiple options, no output schema) and lack of annotations, the description is insufficient. It does not explain what the returned proposal looks like, that model is required, or how it relates to sibling tools like send_kp_email. The description only offers a high-level overview without operational details.

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

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

Schema description coverage is 100%, so the baseline is 3. The tool description adds contextual context by listing the proposal sections, which loosely map to parameters like extras, but it does not explain how parameters affect the output or clarify the nested extras structure beyond what the schema already describes.

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 explicitly states the tool generates a commercial proposal (КП) for a turnkey pool, and lists the 12 included sections. This clearly identifies the action and resource, distinguishing it from siblings like send_kp_email (which sends) and calculate_total (which calculates totals).

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 does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives like send_kp_email or get_pool_price. The intended usage is implied by the tool name and description but not stated, leaving the agent to infer when this tool should be selected over related 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?

There are no annotations and the description gives no behavioral information such as read-only nature, output format, or potential side effects. The agent is left to infer from the name that this is a retrieval tool, which is not explicitly disclosed.

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 extremely short and front-loaded, with no wasted words. However, it is a fragment rather than a complete sentence, which slightly undermines its clarity while still being appropriately concise for a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with no output schema, the description still fails to explicitly state that the tool retrieves or lists accessories, leaving the agent to infer the action from the name. The list of categories gives some context but not enough to fully understand the tool's invocation and return behavior.

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

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

With zero parameters, the schema fully documents the input, and the baseline is 4. The description adds a bit of context by listing the specific types of accessories, but since there are no parameters, no additional parameter meaning is needed.

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 lists accessory categories (technical pits, stands, covers, LagunaBox) which clarify the tool's scope but uses no verb, coming across as a label rather than a statement of function. It is more informative than a pure tautology but still lacks a clear action.

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; the description does not mention any conditions, prerequisites, or refer to sibling tools. It simply names accessory types without indicating selection criteria.

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

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

With no annotations, the description must carry the full burden of behavioral disclosure. It lists package contents but does not describe the output format, how the 'model' and 'format' parameters affect the response, whether it returns files or text, or any limitations. This lack of detail leaves significant behavioral ambiguity.

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 efficiently lists the package components. While it lacks structure (e.g., no formatting or examples), it is appropriately sized and front-loaded with the target audience, so it earns its place without unnecessary verbosity.

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 bundles multiple asset types and has no output schema or annotations, the description is incomplete. It does not clarify the return format, the difference between summary and detailed packages, or the behavior when model='all'. It also fails to explain how this relates to sibling tools that offer individual components, leaving the agent with unresolved questions.

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 descriptions cover both parameters (model and format) with 100% coverage, so the schema already conveys their meaning. The description adds no additional parameter semantics, such as examples or default values, thus the schema carries the load and baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific audience (architect/designer/engineer) and enumerates the package contents (pool specs, BIM/CAD files, installation requirements, excavation dimensions), making the tool's purpose evident. However, it does not explicitly state the action ('retrieve' or 'get') and does not differentiate from sibling tools like get_bim_files or get_pool_specs.

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 the sibling tools that provide individual components (e.g., get_bim_files, get_pool_specs). The intended use is only implied by the target audience, with no explicit when-to-use or alternatives.

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

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

With no annotations provided, the description must disclose behavioral traits itself. It lists supported file formats but does not clarify whether the tool downloads files, returns a list of links, or has any side effects or authorization requirements. The read-only nature is implied but not stated, leaving a transparency gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence that front-loads the core purpose and file types. Every word earns its place, though it could be slightly more explicit about the action (e.g., 'download').

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 one optional parameter, no output schema, and no annotations, the description covers the core purpose and supported file types. The schema covers the parameter, so the description is sufficient for basic invocation, though it could mention whether output is a list or download.

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 full coverage for the sole parameter 'model' with a description of allowed values and default behavior. The description adds no parameter-level information, so it meets the baseline without exceeding it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as providing BIM/CAD files (DWG, OBJ, RFA, 3DS) for architects and designers, which distinguishes it from sibling tools like get_furniture or get_slides. It lacks an explicit verb like 'download' but the noun phrase is unambiguous.

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 includes a usage context ('Для интеграции бассейна в проект' – for pool integration), which tells the agent when this tool is appropriate. It does not mention alternative tools or exclusions, so it stops short of full guidance.

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 description is only a noun phrase with no behavioral information. There are no annotations, so the description should disclose what the tool does (e.g., returns a list, is read-only, any limitations). It doesn't mention any of this, making it impossible to know the tool's behavior or side effects.

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 concise, a single short phrase with no unnecessary words. It's front-loaded with the subject. However, it may be overly terse, bordering on under-specification, but that's a completeness issue rather than 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 no output schema and no annotations, the description should explain what the tool returns and its behavior. It only gives a subject label, leaving out the action and any return information. This is inadequate for an agent to select and invoke the tool 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, so the baseline is 4. The description doesn't need to explain parameters because there are none. It neither adds nor detracts from parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource (LPF filtration units for pools, 400-900 mm) but lacks an explicit verb like 'get' or 'list'. It's clear what subject it concerns, but not what action it performs, leaving the tool's purpose to be inferred from its name.

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. It doesn't mention any prerequisites, exclusions, or differentiate from sibling tools like get_pool_specs or get_accessories. The context implies it is for retrieving filter info, but that's not stated.

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 must explain behavior. It states that this is a catalog and enumerates furniture types, but does not disclose whether it returns a static list, whether images are included, or any other behavioral details beyond the basic catalog concept.

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 with the key concept front-loaded ('Каталог композитной мебели') followed by a helpful list of examples. No filler 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?

For a no-parameter catalog tool, the description is sufficiently complete: it names the domain (pool furniture), material (composite), and examples of included items. Given the absence of an output schema and the simple nature of the tool, little else is necessary.

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 the schema offers no parameter-level information. The description adds value by enumerating the specific furniture categories (loungers, benches, tabletops, stools, planters), giving the agent and user a clear sense of what the catalog contains.

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 tool as providing a catalog of composite furniture for pools, listing specific categories (loungers, benches, tabletops, stools, planters). It distinguishes from siblings like get_accessories by focusing on furniture, although it lacks an explicit verb.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: use this tool when the user needs pool furniture items. However, there is no explicit guidance on when to choose this over similar sibling tools like get_accessories or get_pool_specs.

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 must carry the full burden of behavioral disclosure. It only states that this is an official guide, but does not disclose any behavioral traits such as whether the tool is read-only, what the response format is (e.g., text, PDF), or if there are any side effects. For a simple retrieval tool, this is a significant omission.

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 phrase that lists the content areas without verbose repetition. It front-loads the key resource name and purpose. However, it lacks a proper verb (e.g., 'retrieves' or 'provides') and is not structured as a full sentence, which slightly detracts from its directness, but overall it is efficient and free of fluff.

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 (one parameter, no output schema, no annotations), the description is minimally viable. It tells the agent what the passport covers and implies it is a reference document, but it does not explain the output format or how the 'section' parameter influences the response. The description is adequate for basic comprehension but lacks detail that would help the agent anticipate the tool's behavior.

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

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

The schema already provides a comprehensive description of the 'section' parameter with all enum values, achieving 100% coverage. The description adds no additional parameter-level details beyond the schema, so the baseline of 3 is appropriate. The description's topical list roughly aligns with the enum values, but it does not clarify each option beyond what the schema already does.

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 resource ('Паспорт бассейна Laguna Pools') and the topics covered (installation, operation, conservation, care, water treatment, additional equipment), making it apparent that the tool retrieves an official pool guide. However, it does not differentiate this tool from siblings like get_pool_specs or get_architect_package, which could also deal with pool-related documentation.

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 explicit guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or alternative tools. The section parameter in the schema hints at usage, but the description itself gives no usage context, leaving the agent to infer when to select this tool.

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, and the description does not disclose any behavioral aspects such as read-only nature, response format, or side effects. The description is merely a noun phrase and provides zero behavioral transparency.

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 extremely concise with no filler, which is good. However, it is a fragment rather than a complete sentence, slightly reducing structural clarity.

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 is too sparse. It doesn't indicate return format, whether multiple prices are returned, or any prerequisites, making it incomplete for an agent to use effectively.

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 already documents the single parameter 'model' with 100% coverage (laguna2..laguna9). The description adds context about series options but does not explain how they map to the parameter, so it adds only marginal value 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 indicates the tool provides pool prices, and adds specificity about PREMIUM and PREMIUM NORD series options. However, it is a noun phrase rather than a full sentence with an explicit verb, and it doesn't explicitly contrast with sibling tools like get_pool_specs.

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 given on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description simply states what the tool returns.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It enumerates the output fields (dimensions, volume, weight, depth, series, warranty), implying a read-only lookup, but does not disclose potential errors, data formats, or any side effects. It adds some context beyond the schema (fields returned) but leaves the safety profile implicit.

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 immediately states the tool's purpose and lists the specific attributes, making it efficient and scannable with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple (1 parameter, no output schema), and the description lists the returned characteristics, which covers the main return values. However, it lacks any context about parameter formatting, model validation, or what happens with invalid input, making it minimally complete but not thorough.

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 single parameter 'model' is fully described in the schema with an example range (laguna2..laguna9), so the description adds no additional parameter meaning. At 100% schema coverage, the baseline score is 3; the description's mention of 'series' and 'warranty' likely refers to output fields, not the input.

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 detailed pool specifications including dimensions, volume, weight, depth, series, and warranty, which distinguishes it from sibling tools like get_pool_price or get_accessories. The lack of an explicit verb in the description (relying on the tool name) is minor, but the resource and scope are unambiguous.

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 like search_pools, recommend_pool, or get_pool_price. There is no mention of prerequisites, intended use cases, 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?

No annotations are provided, so the description carries the full burden. It only gives domain information (ecoFINISH technology, coatings) without stating that the tool is read-only, what it returns, or any side effects. For a 'get' tool, this is a significant lack of behavioral disclosure.

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 very short and concise, with no wasted words. However, it is under-specified: it reads as a fragment rather than a structured description and does not adequately front-load key information like return type or usage.

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 only one parameter and no output schema, the description should clarify what the tool returns and how the service_type parameter affects results. It does neither, so the description is incomplete for a user to understand the tool's functionality.

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% (the one parameter has a description and enum). The description text adds no parameter information, but per the baseline for high schema coverage, a 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 states the domain: repair and reconstruction of pools, which aligns with the tool name. It also mentions a specific technology (ecoFINISH) and coating types, adding scope. However, it does not explicitly use a verb like 'returns' or 'lists,' so it's clear but not fully explicit.

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 implies it is for repair/reconstruction services but does not mention any exclusions, prerequisites, or refer to sibling tools. No usage context is provided.

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 of behavioral disclosure. It states that the tool is a 'catalog' which implies a read-only list of items, but it does not explicitly describe the return format, whether the list is exhaustive, any pagination behavior, or whether the data is static or dynamic. The minimal noun phrase lacks the verb structure that would clarify what the tool actually does (e.g., 'Returns a catalog of...').

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 extremely concise, consisting of a single short phrase that conveys the essential purpose. There is no unnecessary wording, and it is front-loaded with the key information. While it is terse, conciseness does not penalize lack of detail—other dimensions address completeness. This is an efficient, well-structured description for its intended 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 and no output schema, the description is the only provided context. It is minimally viable by stating the domain (pool slides and waterfalls), but it lacks important context such as what information the response contains (e.g., prices, dimensions, availability), whether the catalog is static or dynamically updated, or any special behaviors. It could be improved by explicitly stating that it returns a list of available slides and waterfalls and perhaps noting if there are no filtering options.

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 an empty object. With no parameters to explain, the description does not need to provide parameter semantics. The baseline score of 4 is appropriate because there is nothing for the description to add about parameters, and the schema coverage is effectively 100% (vacuously).

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 'Каталог горок и водопадов для бассейнов' clearly identifies the resource (slides and waterfalls for pools) and implies a listing/catalog function. It effectively distinguishes this tool from sibling tools like get_filters and get_furniture by specifying the exact category of pool equipment. However, it relies on the tool name to convey the verb (get), rather than using an explicit verb like 'list' or 'retrieve'.

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. The description does not mention any prerequisite conditions, context, or exclusions. Since sibling tools include other catalog-type tools (e.g., get_filters, get_furniture), users are left to infer that this tool is specifically for slides and waterfalls, but the description does not explicitly state the intended use case.

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

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

With no annotations, the description must disclose behavior, but it only says 'calculator with options.' It does not state that this is a read-only operation, what the return value looks like, or whether any side effects (e.g., saving quotes) occur. The absence of any behavioral detail leaves the agent uncertain about the tool's execution characteristics.

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 concise, with a clear one-sentence purpose followed by a compact list of model types. It is front-loaded with the main verb and resource. Minor redundancy ('Расчёт стоимости' and 'Калькулятор') exists, but it is not wasteful enough to lower the score further.

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 6 parameters and no output schema, the description is thin. It does not explain how the parameters interact (e.g., whether for_pool overrides width/length), what the calculator returns, or any required inputs. The agent is left with limited guidance on how to construct a valid request or 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?

Schema description coverage is 100%, so parameters are already documented. The description adds a Russian-language mapping for the model enum (single, double, width) which slightly enhances clarity, but it does not provide additional meaning beyond the schema, such as how options interact or constraints. Basline 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 calculates the cost of a sliding terrace/pavilion for a pool, which is specific and distinct from siblings like get_pool_price or calculate_total. It also lists the three model types, providing immediate clarity on 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 it is meant for terrace pricing calculations, but it does not explicitly state when to use it versus alternatives or provide any exclusions. There is no mention of prerequisites like pool model or dimensions, so the 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?

No annotations are provided, so the description carries the full burden. It states the tool lists tools, implying a read-only operation, but does not disclose return format, whether descriptions or details are included, or any other behavioral aspects. This is minimal but not misleading.

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 key information. It is appropriately sized for the tool's simplicity, with 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?

For a zero-parameter, no-output-schema introspection tool, the description is complete. It fully conveys the tool's purpose without needing additional context or edge-case handling.

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 with 100% schema coverage, so there is nothing to add beyond the schema. The description correctly omits any parameter details, and the baseline for zero-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 'Список всех доступных инструментов MCP сервера' clearly states the tool lists all available MCP server tools, using a specific verb (list) and resource (all available tools). It is a meta-tool clearly distinct from the domain-specific sibling tools like get_pool_price or search_pools.

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 does not provide explicit when-to-use guidance or mention alternatives. However, the tool's purpose is inherently implied by its name and role as a discovery tool, so usage is understood without being stated.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It labels itself as an 'AI-manager' and 'professional consultation' but does not explain the output format, whether it asks follow-up questions, if it is a read-only recommendation, or any limitations or side effects. The behavior is largely opaque.

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 phrase that front-loads the tool's identity and purpose: 'AI-менеджер: подбор бассейна...'. No redundant or extraneous words. It is appropriately sized and 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?

Even though the 7 parameters are well-described in the schema, there is no output schema and no annotations. The description fails to explain what the tool returns (a recommendation, a set of options?), what optional parameters imply, or how it fits with sibling tools like search_pools or calculate_total. For a consultation tool with 7 optional inputs, this is incomplete.

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%; every parameter has a detailed Russian description. The tool description itself only loosely references 'site, needs, budget' without mapping to specific parameters or their interactions. The baseline score of 3 applies because the schema already carries the parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'AI-менеджер: подбор бассейна по описанию участка, потребностей, бюджета' (AI-manager: pool selection based on site description, needs, budget). This is a specific verb+resource and partially distinguishes from siblings like search_pools by framing it as a consultation. However, it lacks detail on what the recommendation consists of (e.g., a model suggestion, a list, or a report).

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 the user needs a pool recommendation given site, needs, and budget. It does not provide explicit alternatives or exclusions among the many sibling tools, and no 'use X when...' guidance is present. There is no mention of when not to use this tool or how it compares to search_pools or consult_service.

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 present, so the description carries the full burden of behavioral disclosure. It only states the search function and parameters, but does not disclose read-only nature, result format, filtering logic, or any side effects. This is insufficient for a tool with no other safety/behavioral signals.

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 core intent and then summarizes the parameters. It is concise, avoids redundancy, and contains no filler or off-topic content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema and annotations, the description should clarify search behavior, return values, and how parameters interact. It does not mention what the tool returns, whether results are filtered and/ored, or what happens when no parameters are provided. This leaves significant gaps for a 5-parameter fully optional search 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 input schema has 100% coverage with descriptions for every parameter, including enum meanings and units (e.g., 'Бюджет (₽)', 'климат: warm (стандарт) / cold (PREMIUM NORD с утеплением)'). The description merely lists parameter categories and adds no additional meaning 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 the action ('Поиск' / search) and resource (композитных бассейнов / composite pools), and lists the key parameters (dimensions, budget, type, climate). It is specific and informative, but does not explicitly distinguish this tool from sibling tools like recommend_pool.

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 searching for composite pools by the mentioned criteria, but it provides no explicit guidance on when to use it vs alternatives, nor any exclusions or complementary tool references. The usage context is only implied by the search intent.

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

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

With no annotations, the description carries full burden. It states the tool sends an email and depends on generate_kp data, but does not disclose side effects (e.g., irreversibility of sending), error behavior, or return value. This is average transparency for a send 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 two short sentences, front-loaded with the main action and supplemented by a clear dependency note. Every word is useful, no 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 has a nested object parameter, no output schema, and no annotations. The description covers the core purpose and the expected source of data, but lacks details about prerequisites (e.g., 'call generate_kp first'), what the function returns, or how failures are handled. It is minimally complete but not richly so.

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

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

Schema description coverage is 100%, so all parameters have descriptions. The description adds the context that kp_data comes from generate_kp, which is already stated in the schema, so it adds little beyond the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool sends a generated КП (commercial proposal) to the client's email, with a specific verb ('Отправка') and resource ('email клиенту'). It also references generating data from 'generate_kp', which distinguishes it from sibling tools like submit_lead or get_pool_price.

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 explicitly mentions it accepts data from generate_kp, implying a clear usage context: this tool should be used after generating a proposal. It does not explicitly name alternatives or when-not-to-use, but the reference to generate_kp provides strong guidance.

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

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

With no annotations, the description carries the burden. It discloses a key behavioral trait: the client will receive a callback. However, it does not mention other side effects, data handling, or failure modes.

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 short sentences, both essential: one states the action, the other the consequence. 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?

For a simple lead-form tool with fully described parameters and no output schema, the description is adequate. It states the purpose and the resulting callback, which covers the key user expectation.

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 all four parameters. The description adds no additional parameter semantics but doesn't need to.

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 specifies the verb 'submit' and the resource 'lead request to Laguna Pools dealer' for consultation/order. This clearly differentiates it from sibling tools that focus on calculations, lookups, or sending emails.

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 this tool is for submitting a request that results in a callback, providing context for when to use it. It does not explicitly mention exclusions or alternatives, but the context is unambiguous given the sibling list.

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

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

With no annotations provided, the description carries the burden. It transparently describes the async flow: AI prepares a draft and a specialist receives a request, which implies side effects (ticket creation). It also states 'source of truth' and mode=status for checking, adding context 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 moderately long but every sentence provides useful information: topics, flow, and mode=status. It is structured with lists and a clearly described process, making it easy to scan without being redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 7 parameters and no output schema, the description covers the purpose, topics, modes, async workflow, and source of truth. It does not describe the exact return format, but that is partially implied by 'ready answer from database' and 'check answer by number'. Overall, it is adequately complete for a consultation 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?

Schema coverage is 100%, so the schema already explains all parameters. The description adds the overall flow and the mode=status context, but does not add new details about individual parameters beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Техническая консультация по бассейну' (technical consultation on the pool), which is a specific verb+resource. It lists exact topics (drawings, BIM, fittings, foundation) and distinguishes from sibling tools like get_pool_price or generate_kp by emphasizing consultation and the ticket flow.

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 explains the consultation flow (ready answer → miss → request with number) and explicitly mentions mode=status for checking answers. It gives clear context on when to use the tool, though it does not explicitly name alternatives or exclusions.

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