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

No annotations are provided, so the description carries the transparency burden. It discloses important parameter constraints and a security guideline (no API key), but it does not explain what the tool returns, whether it is read-only, or any authentication requirements. This leaves some behavioral aspects ambiguous.

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

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

The description is two sentences long, front-loaded with the primary action, and every word adds value. There is no redundancy or filler; it is a model of concise, structured writing.

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 (8 parameters, 2 required, no output schema), the description covers critical constraints that are not evident from the schema alone. However, it does not mention return values or the available mode parameter, which would improve completeness. Yet, the schema covers those details, so the description is sufficient for basic use.

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

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

The schema provides 100% coverage of parameter descriptions, giving a baseline of 3. The description adds value beyond the schema by stating that exactly one of image_url, image_base64, or image_path must be provided, which is not enforced in the schema. It also reinforces the security note about not passing an API key.

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: analyzing an image using a user-specified cloud vision model. It uses a specific verb (analyze) and resource (image with vision model), and it distinguishes itself from sibling tools like vision_list_models, vision_get_setup_guide, and vision_test_connection by focusing on the analysis action itself.

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 explicit usage constraints: must provide provider_id, exactly one of image_url/image_base64/image_path, and do not pass API keys. While it does not explicitly name alternative tools for when not to use this one, the constraints and the tool's dedicated role make the usage context clear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states that the tool will not automatically select or enable any models, which is a useful non-mutation guarantee. However, it does not disclose return format, whether it is purely read-only beyond the stated behavior, or any other limitations.

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 sentences, front-loaded with the primary purpose and followed by a brief non-action statement. Every word earns its place; 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 simple tool with no parameters, no output schema, and no annotations, the description is complete. It states what the tool returns, what it does not do, and is adequate for an agent to understand its function and side-effect profile.

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 description coverage is trivially 100%. Per guidelines, a 0-parameter tool receives a baseline of 4. The description adds no parameter semantics because none exist.

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

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

The description clearly states the tool returns visual model configuration instructions and recommended suppliers. This specific verb-resource pairing ('returns... instructions') differentiates it from siblings like vision_list_models (lists models), vision_analyze (analyzes), and vision_test_connection (tests connections).

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

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

The description provides clear context: use this tool when you need configuration instructions and supplier recommendations. The explicit 'will not automatically select or enable any models' implies it is a reference guide, but it does not explicitly name alternative tools or provide when-not-to-use conditions, so it stops short of a 5.

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

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

With no annotations, the description carries the full burden. It explicitly states that API keys are not returned and that there is no default vision model, which are important behavioral traits. It does not disclose auth requirements or response format, but for a simple listing tool these are less critical.

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, two sentences long, with the main purpose front-loaded. Every sentence adds value: the first states what is listed, the second adds a security-relevant caveat and usage context.

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 listing tool, the description provides sufficient context: it names the output scope, notes that API keys are not returned, and gives usage guidance about provider_id. It could mention the structure of the list, but this is a minor gap given the simple nature of the tool.

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

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

The tool has 0 parameters, so the baseline is 4. The description adds no parameter-specific information, but none is needed. The schema coverage is effectively 100% since there are no params to document.

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 '列出' (list) and names the resource '所有视觉模型配置' (all vision model configurations), making it clear what the tool does. It also distinguishes from sibling tools by mentioning vision_analyze's requirement for an explicit provider_id, implying this tool is used to discover available models.

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 indirectly guides usage by stating that vision_analyze has no default model and requires explicit provider_id, suggesting this tool is useful for finding that ID. However, it does not explicitly say 'use this tool when you need to list models' or contrast with vision_get_setup_guide or vision_test_connection, so it's clear but not fully 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool does not accept an API Key, that perform_request sends a minimal text request, and that the default is config-only. This adds meaningful behavioral context beyond the schema, though it does not explicitly state whether the operation is read-only or has 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 two sentences, front-loaded with the core purpose, and contains no filler. Every clause adds relevant information about behavior and constraints, making it highly efficient.

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

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

For a simple tool with two parameters and no output schema, the description covers purpose, default behavior, optional request mode, and a key constraint. It lacks explicit details about return values or error handling, but for a test-connection tool this is adequate and matches the tool's 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 baseline is 3. The description adds value by emphasizing that perform_request=true sends a minimal request to verify authentication, and by clarifying that provider_id refers to a vision model. It also sets expectations by stating that no API Key is accepted, which indirectly informs parameter use.

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 '测试某个已配置 provider 的连通性' – a specific verb (test), resource (configured provider), and scope (Key/Model/Endpoint). It also distinguishes from siblings like vision_list_models (listing) and vision_analyze (analysis), making the tool's unique purpose clear.

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

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

The description explains two usage modes: default config-only check and perform_request=true for actual authentication verification. It also notes that it does not accept an API Key, implying it is for pre-configured providers. However, it does not explicitly mention alternatives or when not to use this tool, missing an opportunity for clearer differentiation.

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