Plant-Based Nutrition Protocols

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

Annotations already indicate readOnlyHint=true, so the description appropriately reinforces that the tool is safe to call. It adds value by detailing the returned fields (therapeutic foods, meal structure, etc.), providing context beyond annotations. No contradiction.

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, information-packed sentence. It front-loads the core action and lists output items, but the length could be slightly trimmed without losing clarity.

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 3 parameters, no output schema, and a nested object, the description should explain user_context and duration_weeks. It mentions clinical citations but not how user_context modifies results or that duration_weeks defaults to 4. The description is minimally viable but has gaps.

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%, yet the description does not explain any of the three parameters. While condition's enum is self-explanatory, user_context and duration_weeks are left without meaning, requiring the agent to infer or guess their purpose.

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 generates an evidence-based whole-food plant-based protocol for 47 chronic conditions, listing specific output components. The verb 'Generate' and resource 'protocol' are precise, and the 47 conditions distinguish it from sibling tools like query_nutrition_topic.

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 for obtaining a protocol for a condition but does not provide explicit guidance on when to use versus alternatives or when not to use. Sibling tool query_nutrition_topic is present but no differentiation is offered.

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?

Annotations already provide readOnlyHint=true, so the description adds little behavioral context beyond noting the knowledge base size. No contradictions, but minimal additional value over structured data.

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?

Single, front-loaded sentence conveys the core purpose efficiently with no extraneous 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?

Given no output schema, the description does not specify return format or fields. It also does not mention pagination or behavior when no results found. Adequate but lacking details for a smooth agent experience.

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%, yet the description does not explain the 'max_results' parameter at all (range, effect). The 'query' parameter is implied but not defined. Significant gap in parameter documentation.

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 that this tool answers nutrition or food-as-medicine questions using a knowledge base, distinguishing it from the sibling 'get_protocol' which likely retrieves specific protocols.

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 explicit guidance on when to use this tool versus 'get_protocol'. The description implies it's for general questions, but lacks clear demarcation or examples of when not to use it.

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