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

With no annotations provided, the description carries the full burden. It discloses the main behavior (submit answer, return next question) but omits details like whether the session object is mutated, validation messages, or requirements for an active session. This is partially transparent but not comprehensive.

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, directly stating the action and result with no 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 simple two-parameter tool with a nested session object, the description covers the core operation. It lacks detail on the exact structure of the returned next question, but this is not critical for invocation. Overall, it is sufficient 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%, with descriptions for both 'score' and 'session'. The description adds no new parameter information beyond the schema's min/max constraints and object structure.

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: submit a score (1-5) and retrieve the next question. It distinguishes itself from siblings like list_tests, start_test, get_progress, and calculate_result.

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 this tool is used during an active test session to record an answer and advance to the next question. It doesn't explicitly mention when not to use it or compare with alternatives, but the context is clear from the action name and description.

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

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

With no annotations, the description alone must disclose behavioral traits. It only states what the tool does (calculates result) but does not indicate side effects, state changes, permission requirements, or the nature of the output. This is a critical gap for a tool that likely operates on a session.

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 wasted words, which is concise in form. However, it is so minimal that it borders on under-specification, providing only a bare action without context or structure, similar to the 'Process' example.

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 lack of annotations, no output schema, and a single undocumented nested parameter, the description carries full responsibility for completeness. It fails to explain what the final test result is, how it is computed, what the return value looks like, or any required session structure, making it grossly inadequate.

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 shows one required 'session' object with 0% description coverage, and the tool description does not explain what 'session' should contain or how it relates to calculating the result. The agent is left with no information to construct a valid session object, making correct invocation impossible.

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 '计算最终测试结果' clearly states the tool calculates the final test result, with a specific verb and resource. However, it does not differentiate from sibling tools like 'get_progress' or 'answer_question', which might also involve result-related actions, so it lacks explicit distinction.

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. There is no mention of prerequisites, typical scenarios, or why one would choose 'calculate_result' over 'get_progress' or other siblings, leaving the agent with no context for appropriate invocation.

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. The verb '查询' (query) implies a read-only operation, but there is no disclosure about session requirements, return format, or side effects. Minimal behavioral context beyond the name.

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 is front-loaded with purpose. It is appropriately brief for a simple tool, though it borders on under-specification by omitting useful details.

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 one nested object parameter and no output schema, the description lacks essential information about the session object and the nature of the returned progress. It is incomplete for an agent to invoke correctly without additional assumptions.

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 defines a single required 'session' object but provides no description, and schema description coverage is 0%. The description does not mention 'session' at all, failing to compensate for the opaque parameter.

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 '查询当前测试进度' clearly states the tool queries current test progress using a specific verb and resource. It distinguishes from siblings like list_tests and start_test, which serve different purposes.

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

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

No guidance is provided on when to use this tool versus alternatives like list_tests or start_test. The description gives no context about prerequisites (e.g., an active session) or scenarios where this tool is preferred.

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 alone must convey behavioral traits. The verb '列出' implies a read-only operation, and the scope '所有11种' is disclosed. However, it does not describe the return format or any other behavioral details, which would be beneficial for a tool with no annotations.

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

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

The description is a single, clear sentence that conveys the purpose without any waste. It is appropriately sized for the tool's simplicity.

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

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

Given the tool's simplicity (no parameters, no output schema, no annotations), the description sufficiently defines its function. It lacks usage context, but that is already captured in the usage guidelines dimension.

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 description does not need to explain parameters. The baseline for 0-parameter tools is 4.

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

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

The description clearly states the tool's function: '列出所有11种可用的测试类型' (List all 11 available test types). The verb '列出' and resource '测试类型' are specific, and the count '11种' adds precision. It distinguishes from sibling tools like start_test and answer_question, which are action-oriented.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, alternatives, or exclusion cases. The use case must be inferred from the tool's name and sibling 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that it starts a test but does not mention side effects (e.g., session creation, progress reset), prerequisites, or what the response contains—critical gaps for a state-changing action.

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 the core action stated first. The long list of test types is necessary but creates a minor readability burden; overall, every element earns its place.

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

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

For a tool with one parameter and no output schema, the description adequately covers the test type enumeration. However, it omits behavioral context (e.g., whether the test starts immediately, how the agent should proceed) and does not clarify return behavior, leaving the agent partially in the dark.

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?

Though schema coverage is 100% with an enum, the description adds semantic value by mapping each enum value to a human-readable Chinese explanation (e.g., MBTI simplified, Enneagram deep version). This goes beyond the bare schema definition, helping the agent select the correct test type.

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 'start' and clearly identifies the resource (personality test), while listing all available test types. This differentiates it from sibling tools like list_tests, answer_question, get_progress, and calculate_result, which serve other stages of the workflow.

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: if you want to begin a personality test, this is the tool to use. However, it provides no explicit when-not-to-use guidance or references to alternatives, leaving the agent to infer context from sibling tool names.

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