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

With no annotations, the description carries full burden. It discloses the core behavior (calculating and interpreting) and the mocked nature, but leaves ambiguity about what 'mocked' means (answers vs. calculation) and does not describe the output format, score range, or how missing/invalid answers are handled.

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 with no filler. It front-loads the verb and resource, and the parenthetical 'mocked' adds an important caveat 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?

The tool is simple with one well-documented parameter, but lacks an output schema. The description mentions 'interpret' but does not specify what the returned interpretation looks like or whether the score is numeric, categorical, etc., so it is not fully complete.

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 100% coverage of the single 'answers' parameter with a clear description ('A dictionary mapping question IDs to numeric scores'). The tool description adds no new parameter semantics beyond restating that user answers are used.

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 ('calculate and interpret') and a clear resource ('SRI score'), distinguishing it from sibling tools like get_sri_questionnaire (fetching questions) and search_sexual_psychology_kb (searching). The phrase 'based on user answers' clarifies the input source.

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 you have user answers and need an SRI score, but does not explicitly state when to use this vs. alternatives. The '(mocked)' parenthetical provides a caveat about the tool's nature but no concrete when-not-to-use 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. It only states that the tool retrieves information and sample questions, but does not disclose the return format, any authentication needs, whether the content is static/dynamic, or other behavioral traits.

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 the action and resource. No filler or repetition, earning top marks.

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 tool with no output schema, the description is largely sufficient. It clearly states what the tool provides, though it could explicitly mention that no input is required or elaborate on the output format. Given the tool's simplicity, this is a minor gap.

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 to describe. Baseline 4 applies because the description does not add parameter details, but none are 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 uses a specific verb ('Get') and resource ('information and sample questions for the Sexual Repression Index (SRI) quick assessment'), clearly distinguishing it from sibling tools that calculate scores or search a knowledge base.

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 you need SRI info/questions) but does not explicitly mention alternatives or when not to use this tool. No guidance is given regarding `calculate_sri_score` or `search_sexual_psychology_kb`.

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 disclosing behavioral traits. It says 'Retrieve' (implying read-only) but does not explicitly state whether the operation is safe, what the output format is, or any limitations (e.g., exact topic matching, result volume). This lack of detail leaves significant ambiguity for the agent.

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, focused sentence that gets straight to the point. Every word contributes value, and the examples are integrated naturally without padding. It is optimally 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 simple one-parameter search tool with no output schema, the description covers the core purpose and provides examples. However, it lacks details about the return value (e.g., a list of summaries, article titles) and any constraints on the input. The lack of annotations increases the need for more behavioral context, which is not provided.

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 100% coverage for the 'topic' parameter with a description and an example. The tool description adds a couple more examples (sexual-guilt, sexual-shame), offering slight additional context, but it does not explain parameter semantics beyond the schema, such as whether the topic should be an exact keyword or natural language phrase.

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 with a specific verb ('Retrieve') and resource ('scientific background knowledge regarding sexual psychology'). It provides concrete examples (dual-control-model, sexual-guilt, sexual-shame), which distinguishes it from sibling tools that handle questionnaires and scoring.

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 appropriate context: use when needing scientific background on sexual psychology topics. The examples clarify the types of topics, and the contrast with sibling tools (questionnaire retrieval/scoring) provides implicit guidance, but no explicit exclusions or alternative recommendations are given.

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