update_recommendations

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 states that recommendations are updated, but does not disclose side effects (e.g., overwriting existing recommendations), reversibility, required permissions, or whether the update is asynchronous. For a mutation tool, this is a significant transparency 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 sentence with no filler, front-loading the core action ('update user recommendations') and providing the key context ('based on latest user behavior and interests'). Every word 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?

Given that this is a mutation tool with no annotations and no output schema, the description is too minimal. It does not explain return values, errors, or what happens after the update (e.g., whether recommendations are immediately refreshed). The schema covers parameters but not the overall behavior, leaving the agent under-informed for a write 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?

Schema description coverage is 100%, so all parameters are already documented in the input schema. The tool description adds no extra parameter details beyond what the schema provides, but it does not need to compensate since the schema fully covers the parameters. Baseline 3 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 identifies the verb 'update' and the resource 'user recommendations', and distinguishes this mutation tool from the many get_* and analyze_* sibling tools. It also specifies the basis (latest user behavior and interests), making the purpose specific and 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 usage when user behavior has changed ('based on latest user behavior and interests'), but it does not explicitly state when to use this tool versus alternatives like get_user_recommendations or like_article. There are no exclusions or alternative tool mentions, so guidance is only implied.

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