Zavod Stanki CNC Catalog

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 only mentions that an API key is required, which is a prerequisite. It does not disclose any side effects, response format, or what happens after the question is sent. For a tool that likely sends a message to a manager, this is minimal 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 a single short sentence, which is concise and front-loads the core purpose. However, it is under-specified and lacks any structure to guide an agent. It is not bloated, but it omits critical information, making it marginally acceptable.

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 5 parameters, no output schema, and no annotations, the description is severely incomplete. It does not explain the return value, parameter usage, or any prerequisites beyond the API key. An agent cannot confidently invoke this tool correctly based on this description alone.

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 0%, so the description must compensate by explaining parameters. It does not mention any parameter details, not even the required 'question' parameter beyond the tool's purpose. The five parameters (name, email, phone, api_key, question) remain entirely unexplained, providing zero added value over the raw 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 tool's function: asking a question to a live manager. It uses a specific verb ('Задать вопрос') and resource ('живому менеджеру'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools, though the function is distinct among the listed siblings.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention any conditions, exclusions, or alternative tools. There is no context about when asking a manager is appropriate compared to other actions like submitting a lead or searching FAQs.

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 behavioral burden. It discloses that the price is approximate and that spindle/vacuum table are considered, implying a read-only calculation rather than a mutation. It does not say what the return value looks like, whether pricing is live, or whether any configuration state changes occur.

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 deliver the tool's purpose and cost-driving factors with no filler. The first sentence frames it as a configurator and the second specifies the calculation inputs, so the most important information is front-loaded.

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 three-parameter calculator with no output schema, the description supplies the core context: what it calculates, the relevant options, and that the result is approximate. It could be more complete by stating the returned price currency/units or explicitly excluding use cases such as detailed specs or comparisons.

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

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

Schema coverage is 100%, so the baseline is 3 on parameter documentation. The description adds value by explicitly identifying spindle and vacuum table as factors in the price calculation, which tells the agent these parameters affect the result rather than just describing their schema types.

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 concrete action, 'Расчёт ориентировочной стоимости', and the object, 'станков Каменского завода', making it clear this is a price-estimation tool for machines. The mention of spindle and vacuum table differentiates it from catalog/detail siblings like search_machines and get_machine_detail, which do not calculate prices.

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 'онлайн-конфигуратор' and 'расчёт ориентировочной стоимости' imply this tool is for price-estimation tasks with options. However, it never states when NOT to use it or points to alternatives such as get_machine_detail or compare_machines, so the guidance 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?

With no annotations, the description carries the full burden. It says nothing about whether this is read-only, what the comparison output contains, ordering behavior, or what happens if fewer than 2 IDs are supplied.

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

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

A single front-loaded sentence with no waste, though the parenthetical spec list sits awkwardly at the end.

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 comparison tool with no annotations and no output schema, the description is minimally adequate but omits mutuality of ids vs id_a/id_b and any behavioral expectations.

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 documents ids, id_a, and id_b. The description adds the '2–4 machines' range, but not the relationship between ids and the id_a/id_b alternative.

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

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

States a specific verb (compare) and resource (machines) with scope detail (2–4 machines, comparing specs and price). It distinguishes itself from siblings like search_machines or get_machine_detail, though 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?

Implied usage when comparing multiple machines, but no explicit when-to-use guidance or exclusions versus get_machine_detail for single-machine lookups. The 2–4 count constraint hints at scope but isn't framed as 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'active orders' which implies a filter and suggests a read-only operation, but it does not explicitly state that it is read-only, nor does it mention any side effects, pagination, or rate limits. For a simple list tool, this is acceptable but not thorough.

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 core action and scoping. It is concise, with no redundant words or unnecessary details, and directly conveys the tool's purpose without padding.

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 only two optional parameters and no output schema, the description is largely complete. It clearly states what the tool returns (active orders) and the filtering dimension (region/city). The limit parameter is covered by the schema, and the lack of output schema means no expectation of return structure. It could mention that it returns a list of orders, but that is implied by the word 'list'. Overall, it is sufficient for an agent to call 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?

Schema description coverage is 100% (both city and limit are described). The description adds no additional meaning beyond what the schema already provides; it reiterates the city filtering but does not introduce new syntax or semantics. With high schema coverage, a baseline of 3 is appropriate.

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

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

The description clearly states a specific verb ('list') and resource ('active orders for milling and cutting from the Factory's Order Exchange') with a scoping condition (selected region/city). This distinguishes it from all sibling tools, which focus on machines, company info, FAQ, etc., making the tool's purpose immediately obvious.

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 (orders for a region/city) but does not explicitly mention when to use it versus alternatives or when not to use it. However, since no sibling tool deals with orders, the usage is implicitly unambiguous. It would benefit from an explicit statement like 'use this to get active orders' but the absence of competing tools reduces the need.

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 does not state whether the operation is read-only, whether it requires authentication, or what the response format is. For a simple getter, this is minimal but insufficient for full transparency—an agent cannot know side effects or data source behavior. The description only lists content, 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 concise sentence that front-loads the key content (company context) and specifies categories. It is efficient with no fluff. While it could be slightly more structured (e.g., listing specifics), it earns a 4 for appropriate brevity and 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 parameters and no output schema, the description is the sole source of information about what the tool returns. It lists contacts, websites, and discovery URLs, but does not specify the format, structure, or whether these are arrays or objects. For an agent to use the result effectively, it would benefit from more detail about the response shape. The description is adequate for a basic getter but lacks completeness for a tool with no output schema.

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 there is nothing for the description to explain about parameter semantics. Per the baseline for 0 params, this scores 4. The description does not add anything about parameters because none exist, 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 'Контекст компании: контакты, сайты, llms/MCP discovery URLs' clearly states the tool returns company context (contacts, websites, discovery URLs). It is a specific getter for company information and the resource is implied by the name. While it doesn't explicitly differentiate from siblings like get_machine_detail, the focus on company-level data is distinct enough. Score 4 for clarity without strong sibling differentiation.

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 simply lists content without any context about scenarios (e.g., 'use this when you need company contact details or discovery endpoints'). Sibling tools like get_machine_detail or get_faq are not mentioned, so an agent must infer usage from the name alone. This is a gap for a getter 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 supplied, so the description carries the full behavioral burden. It discloses that the lookup can be keyword-driven and that the corpus is informational/read-only by nature, but says nothing about how many answers come back, whether an unmatched query returns an empty result or a fallback, or the response shape. Adequate for a simple read tool, but thin given zero annotation coverage.

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 with no filler; the topical scope is front-loaded and the query hint follows. Efficient and readable.

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

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

For a one-optional-parameter read-only FAQ lookup with no output schema and no annotations, the description supplies enough to call it correctly — what it covers and how to narrow results. Only the absence of any behavioral/return expectations keeps it from a 5.

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% and the single parameter already documents its keyword semantics with examples, so the baseline is 3. The description's note that query may be passed for FAQ search adds only marginal 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 states a specific resource — answers to frequently asked questions — and enumerates the covered topics (factory, machines, prices, warranty, delivery, training, payback), so an agent knows exactly what content lives here. It does not explicitly differentiate itself from the nearest sibling (ask_manager or get_company_info), which keeps it just below a 5.

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

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

Usage is only implied: the reader infers this is the tool to call for canned FAQ content and that the optional query narrows the lookup. There is no statement of when to prefer this over ask_manager for a general question, nor any exclusion or prerequisite.

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 behavioral burden. It does convey that the tool returns a comprehensive machine card rather than a summary, and 'get' implies a read-only lookup. It does not discuss error behavior, authorization, or what happens when the id is invalid, but for a simple retrieval tool the stated behavior is reasonably clear.

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

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

The description is a single, front-loaded sentence with no filler. It is appropriately brief for a one-parameter lookup tool, though slightly more context about usage would have made it more helpful.

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 get-by-id tool, the description and schema give an agent enough to call it. However, with no output schema and no mention of what fields constitute the 'full card', return expectations are left vague.

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 sole parameter as the machine's product_number, so the description adds little beyond restating that relationship. With 100% schema description coverage, the baseline of 3 is appropriate.

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

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

The description clearly names the operation (retrieving the full machine card) and the lookup key (product_number/id). It distinguishes itself from list-style tools like search_machines by emphasizing 'full card', but it does not explicitly name any sibling it contrasts with.

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: call this when you need a complete machine record by product_number. However, there is no explicit guidance about when to prefer this over search_machines, compare_machines, or get_machine_images, and no exclusions or prerequisites are 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, the description carries the full burden of behavioral disclosure. It only states 'high-resolution images' and does not clarify whether the tool returns URLs, binary image data, multiple images, or any required permissions. The read-only nature is implied but 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 a single short phrase with no filler words. The key qualifier 'high resolution' is front-loaded and there is nothing redundant, making it very 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 one-parameter tool with no output schema or annotations, the description omits essential call-time information: what the response structure is, whether multiple images are returned, and what the 'id' parameter represents. An agent would struggle to invoke this confidently based on the description alone.

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 has a single 'id' parameter with no description, and the tool description does not explain that 'id' refers to the machine or what format is expected. With 0% schema description coverage, the description fails to compensate, leaving parameter meaning to be guessed from the tool name.

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 identifies a specific resource—high-resolution machine images—and the image focus distinguishes it from siblings like get_machine_detail. However, it is a noun phrase with no explicit verb like 'retrieves' or 'returns', so it does not fully meet the top tier.

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 such as get_machine_detail or search_machines. There are no conditions, exclusions, or alternative tool mentions, so the agent must infer usage from the name and phrase 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?

With no annotations, the description carries the behavioral burden. 'List' implies a read-only operation本体 and 'с количеством' adds useful output detail (counts), but the description does not disclose response format, ordering, pagination, or what exactly the quantity represents.

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, front-loaded phrase with no filler. Every word earns its place by identifying the resource and a key output attribute.

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, the description is minimally viable, but it lacks explicit usage context and output shape details. There is no output schema to clarify what fields the agent will receive, and the description does not mention whether the count refers to machines per category or something else.

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)Skip parameter semantics are not needed. The description's mention of 'с количеством' is a mild addition but no parameter documentation is required 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 states the resource ('категорий станков' – machine categories) and the key detail that counts are included ('с количеством'). This clearly differentiates it from siblings like search_machines and get_machine_detail, which operate on machines rather than categories.

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 when an agent should call list_categories instead of search_machines or get_machine_detail, leaving the usage decision entirely to inference.

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

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

No annotations are provided, so the description carries the full burden. It does not disclose read-only behavior, return format, pagination, or any side effects. For a search tool, this is a significant gap.

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

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

A single, concise sentence that front-loads the tool's purpose. No wasted words, though it could add a bit more guidance without sacrificing 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?

For a tool with 8 optional parameters, no output schema, and no annotations, the description is too sparse. It does not explain how parameters interact, return structure, or usage boundaries. An agent would need to guess at behavior.

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

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

Schema coverage is 50% (4 of 8 params have descriptions). The description adds meaning for type, working_area, and material, but does not explain q vs structured filters or price parameters. It partially compensates for the coverage gap but not fully.

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 verb 'search' and the resource 'CNC machines', and lists specific filter dimensions (type, field dimensions, material). It naturally differentiates from sibling tools like get_machine_detail (single machine details) and compare_machines (comparison) by focusing on searching/filtering.

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 finding machines by criteria, but does not explicitly state when to prefer this over alternatives like get_machine_detail or list_categories. No exclusions or contextual hints are 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. It discloses the API-key requirement, but it does not describe side effects, whether the submission is duplicable, what happens on success/failure, or any manager-notification behavior. This is a sparse behavioral picture for a mutation 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 with no filler. The action and the essential prerequisite are stated directly, making it appropriately concise for what it conveys.

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 six-parameter action tool with no annotations, no output schema, and no parameter descriptions, this one-line description is insufficient. It does not explain expected behavior, return value, or the meaning of key parameters, so an agent would struggle to invoke it correctly without guessing.

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 0%, so the description should compensate for the six parameters. It only clarifies the API key requirement; name, email, phone, comment, and machine_id are left to their bare property names, and machine_id is ambiguous.

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 and resource: 'Отправить заявку менеджеру' (submit a request to a manager). It also adds a key prerequisite, 'нужен API-ключ', and the action is clearly distinct from the read/search-oriented sibling 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 implied use case is sending a lead/request to a manager, and the API-key note provides a condition. However, it does not differentiate from the sibling 'ask_manager' tool or state when not to use it, leaving the selection partly to inference.

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

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

No annotations are provided, so the description carries full responsibility for disclosing behavior. It implies a state-changing side effect (creating a webhook subscription) but does not explain the delivery contract, whether subscriptions are idempotent, how the webhook will be authenticated, or any operational details expected for a webhook-based 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 well-formed sentence that states the action and scope in a compact, front-loaded manner. No words are wasted and all key elements appear in the first clause.

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 state-changing tool with no annotations and no output schema, the description is too thin. It does not clarify what happens after subscribing, whether success is confirmed, how the agent is identified, or what delivery format is expected. An agent cannot reliably predict the outcome of invoking this 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 description coverage is 67%, with agent_id and webhook_url already described in the schema. The description's main extra contribution is translating the event_types enum into human-readable Russian terms ('изменении цен, новых заказах Биржи или складских обновлениях'), which adds some semantic value but does not substantially exceed what the enum values already convey.

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 ('подписать' / subscribe) with a clear resource (AI agent) and specific event categories (price changes, new exchange orders, stock updates). This clearly distinguishes it from all sibling tools, which are read-only query 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?

There is no guidance on when to use this tool versus alternatives, no prerequisites, and no mention of when subscription would be more appropriate than polling siblings like get_birzha_orders or get_machine_detail. Usage context is entirely left to the agent to infer.

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