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 mentions support for dialogue with a system prompt, but does not disclose typical behavioral aspects such as authentication needs, rate limits, token usage, side effects, or response format. The description is too thin for a tool with 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?

The description is a single concise sentence that front-loads the primary purpose and includes a key feature (dialogue support). It has no fluff, though it could have expanded on behavior without losing 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?

The tool has no output schema and no annotations, so the description must explain return values and other contextual details. It does not mention what the tool returns or how to handle responses. For a tool with 5 parameters, the description is too sparse to be fully self-contained.

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

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

Schema description coverage is 100%, so the schema already documents all parameters (model, top_p, messages, max_tokens, temperature) with descriptions. The description adds no additional semantic detail beyond what the schema provides, so the baseline score of 3 applies.

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

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

The description states a clear verb ('генерация текста') and resource ('GigaChat'), and distinguishes itself from siblings like generate_image and embed_text. The mention of dialogue with system prompt further refines its 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 no guidance on when to use this tool versus alternatives. It does not mention any conditions, exclusions, or sibling comparison. While the name 'chat' implies conversational use, there is no explicit direction.

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 only states what the tool does (get embeddings) without revealing return format, authentication requirements, rate limits, side effects, or any constraints. For a tool that likely returns substantial data (embedding vectors), 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, clear sentence that immediately states the purpose without any fluff. It is front-loaded with the action ('Получение векторных представлений') and efficiently conveys the essential information. 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?

The tool has no output schema and no annotations, so the description must explain the result and any usage constraints. It does not mention what the output looks like (e.g., array of vectors), any input limits (e.g., batch size), or error handling. For a tool that produces complex output, this is critically 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 coverage is 100% (both 'input' and 'model' have descriptions), so the schema already documents parameter meaning. The description adds no further semantic detail beyond what the schema provides, aligning with the baseline of 3 for full schema coverage.

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

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

The description explicitly states the tool returns vector representations (embeddings) for texts via GigaChat, using a specific verb ('Получение') and resource ('векторных представлений'). This clearly distinguishes it from siblings like chat, generate_image, or get_token_count. An agent can immediately understand the tool's unique role.

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 (use when you need embeddings) by stating the function, but it provides no explicit guidance on when to prefer this over alternatives, nor any exclusions or prerequisites. Usage is inferred from the purpose, qualifying as 'implied usage' rather than 'clear context' or explicit routing.

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 disclosure burden. It states the purpose but does not mention that the upload is a write operation, any authentication or rate-limit requirements, or what the tool returns (e.g., a file ID). For a mutation tool, this is a notable 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, efficient sentence with zero fluff. The purpose is front-loaded, and every word adds value. It is 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?

The description is adequate for a straightforward upload tool, covering the main purpose. However, it does not explain the output format or any special behaviors (e.g., size limits, supported file types, error handling), which could be relevant for an agent to make a successful call. This is a minor omission given the simplicity.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description adds no extra insight beyond the schema (e.g., that file_content should be base64, which is already in the schema). It meets the baseline but provides no value beyond 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 uploading a file to GigaChat for multimodal processing, using a specific verb and resource. It does not explicitly name sibling tools for differentiation, but the siblings (chat, models, etc.) are obviously distinct in function, so the purpose 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 implies when to use the tool (for multimodal processing) but provides no explicit guidance on when not to use it or alternative tools. Since there are no close siblings, the implied usage is adequate, but it lacks proactive routing 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?

With no annotations provided, the description carries the full burden. It only states the action (generate image) and one detail (description in text), but discloses nothing about side effects, permissions, costs, rate limits, asynchrony, or failure modes. For a generative 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?

The description is a single short sentence, which is efficient but under-specified. It front-loads the core purpose but omits important context. It is concise, but conciseness should not sacrifice necessary information; this is borderline under-specification.

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 messages array and a model parameter, but the description does not explain expected output (e.g., image URL), model choices, or any constraints. Without output schema or annotations, the agent has limited context to call it correctly. The description is too minimal for the tool's complexity.

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

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

Schema coverage is 100% and the description adds nothing beyond what the schema already provides. The note 'описание в тексте сообщения' restates the content property's purpose, which is already defined in 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 generates images via GigaChat, which distinguishes it from sibling text-based tools like chat and embed_text. However, it does not explicitly name any siblings or contrast itself, so it's clear but lacks explicit 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?

The description implies usage for image generation but gives no explicit guidance on when to choose this over alternatives, nor any prerequisites or exclusions. It is left to the agent to infer the tool's purpose from its name and the GigaChat reference.

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 full responsibility for behavioral disclosure. It states a check operation, implying read-only, but does not explicitly mention side effects (likely none), permissions, rate limits, or what the response contains. For a simple operation this is minimal, but without annotations it should at least state it's read-only.

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 in Russian, directly stating the function without any filler. It is optimally concise and front-loaded, earning a perfect score for structure.

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

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

Given the tool's simplicity (no params, no output schema, a read-only check), the description is minimal but acceptable. However, it does not specify the return format (e.g., numeric balance, units, quota components), which could be important for the agent to interpret results. Annotations are absent, so a bit more context would improve completeness.

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

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

The tool has zero parameters, so the schema covers 100% trivially. Per the rubric, a baseline of 4 is given for 0-parameter tools. The description adds no parameter information, but none is needed since there are no inputs. Thus the score 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 checks remaining tokens/quota of GigaChat, using a specific verb and resource. It is understandable, though it doesn't explicitly differentiate from get_token_count, which may also relate to token usage. Still, the purpose 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 provides no guidance on when to use this tool versus alternatives. A sibling tool like get_token_count may have overlapping functionality, but no distinction or context is given. An agent cannot infer the appropriate scenario from the description 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action without any detail on what the tool returns (e.g., a single number, per-text counts), how the model parameter affects behavior, error scenarios, or rate limits. This leaves substantial ambiguity for an agent invoking the 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, compact sentence that front-loads the core purpose. It has zero filler and is easy to parse. However, it is so minimal that it omits relevant context (like return format), which slightly reduces its usefulness despite the concise structure.

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

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

For a tool with no output schema and no annotations, the description should explain what the caller gets back and any important caveats. It does not mention the return value (a number? per-item counts?), whether the 'model' parameter changes tokenization, or any limitations. With only two parameters, the description is incomplete for an agent to call it confidently.

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

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

Schema coverage is 100% – both 'input' and 'model' have descriptions in the schema. The description adds no information beyond what the schema already provides; it merely restates that it counts tokens for the input array. Per the guideline, a baseline of 3 is appropriate when the schema does the heavy lifting, and no additional semantic value is needed here.

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 verb ('подсчёт' = counting) and resource ('токенов' = tokens) and the input type (array of texts), making the purpose clear. It does not explicitly differentiate from sibling tools, but the siblings (chat, embed_text, generate_image, etc.) are obviously different in function, so an agent can distinguish them without additional guidance. One point off for not naming the alternative or the model-specific nature.

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 any other. The description simply says it counts tokens for an array of texts, with no mention of when it's appropriate, what alternatives exist (e.g., chat for generation, embed_text for embeddings), or any prerequisites. An agent must infer usage from the name and schema 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?

No annotations are present, so the description carries the full burden of behavioral disclosure. It only states that the tool lists assistants — it doesn't disclose return format, pagination, sorting, or whether auth is required. For a low-complexity read operation this is somewhat acceptable, but the description adds nothing beyond the bare purpose.

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 fully in the localized language with no filler. It's efficient but somewhat sparse — while appropriate for a simple list operation, it could front-load a hint about the return type without bloating the text.

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

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

For a parameterless listing tool, the description is minimally adequate. An agent knows what it does, but without an output schema or any return-format hint, the agent can't predict the shape of results. The description is complete on inputs but thin on 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?

The tool has zero parameters and an empty schema, so the baseline is 4. There is nothing for the description to clarify about parameters; no compensation 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 'Список доступных ассистентов GigaChat' (List of available GigaChat assistants) states a specific verb ('list') and a specific resource, distinguishing it from siblings like embed_text or generate_image. However, it doesn't explicitly differentiate from the closely related list_models sibling, which is a minor gap.

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 gives no context about scenarios, prerequisites, or how it relates to list_models or chat. An agent must infer when listing assistants is appropriate.

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, but it only says 'list', implying a read operation. It does not mention output format, pagination, rate limits, or any side effects. Minimal information is provided beyond the operation itself.

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 unnecessary words. It conveys the tool's core function efficiently and is appropriately sized for a zero-parameter 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 list tool with no parameters and no output schema, the description is functionally complete. It tells the agent exactly what the tool does. While it doesn't explain how models might be used elsewhere, that is beyond the scope of a list operation.

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

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

The tool has zero parameters, so the baseline is 4. There is nothing to document about parameters, and the schema is empty. The description adds no parameter information, but none is required.

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

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

The description states a specific verb ('list') and resource ('available GigaChat models'), making the tool's purpose immediately clear. It is distinct from sibling tools like list_assistants, so an agent can differentiate it without further context.

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 simply states what it does, without mentioning prerequisites, exclusions, or scenarios where another tool would be more appropriate.

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