update_response

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. While 'Update' implies a mutation operation, the description fails to mention critical aspects: whether this requires specific permissions, if changes are reversible, what happens to existing data not in the 'response' parameter, or error conditions (e.g., invalid IDs). For a mutation tool with zero annotation coverage, this is inadequate.

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 appropriately sized and front-loaded, with the core purpose stated in the first sentence. The parameter list is formatted clearly but includes minimal explanations. While efficient, the lack of behavioral or usage details means some sentences don't earn their place fully, preventing a perfect score.

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 complexity (a mutation tool with nested objects), no annotations, and no output schema, the description is incomplete. It covers basic parameters but omits critical context: behavioral traits (e.g., side effects, error handling), usage guidelines, and output expectations. For a tool that modifies survey data, this leaves too many gaps for reliable agent invocation.

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. It lists all three parameters (sid, response_id, response) with brief explanations, adding basic meaning beyond the schema's titles. However, it doesn't clarify the structure of 'response' (a nested object) or provide examples, leaving significant ambiguity. This partial compensation justifies a baseline score.

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 ('Update') and resource ('a response in a LimeSurvey survey'), making the purpose immediately understandable. It distinguishes from siblings like 'add_response' (creation) and 'delete_response' (deletion) by focusing on modification. However, it doesn't explicitly mention what aspects of the response can be updated, which prevents a perfect score.

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 doesn't mention prerequisites (e.g., needing an existing response), exclusions (e.g., not for creating new responses), or contextual cues (e.g., use after 'add_response' for corrections). With siblings like 'add_response' and 'delete_response' available, this lack of differentiation is a significant gap.

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