GLP Companion Nutrition

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

The description is consistent with the readOnlyHint annotation (returning a protocol overview is a read operation), so there is no contradiction. However, it adds no extra behavioral context beyond the purpose, such as output format, included content, or limitations. The annotation covers the safety aspect, but the description doesn't enrich beyond that.

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 is front-loaded with the action and resource. It is appropriately concise with no wasted words or repetition of schema details beyond the phase list.

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 simplicity of the tool (one enum parameter, no output schema), the description provides a minimal viable overview. However, it fails to contextualize what a 'protocol overview' contains, and it doesn't differentiate from similarly named sibling tools. The description is complete for basic understanding but lacks depth for an agent to fully anticipate the response or choose it confidently amid alternatives.

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 has zero parameter descriptions, so the description helps by naming the phases and clarifying they are GLP-1 therapy phases. However, it essentially repeats the enum values without explaining what each phase entails or how the parameter should be used. This is adequate for a self-explanatory enum but not rich semantic coverage.

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 returns a protocol overview for a specific GLP-1 therapy phase, using a specific verb and resource. It lists the exact phases, which helps clarify scope. However, it does not distinguish this tool from similar sibling tools like get_protocol or glp_protocol.

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?

There is no guidance on when to use this tool versus alternatives such as get_protocol or glp_protocol. No prerequisites, exclusions, or situational recommendations are provided, leaving the agent to guess based on the tool name alone.

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 declare readOnlyHint=true, so the safe read-only nature is covered. The description adds valuable behavioral context by enumerating the exact outputs (protein floor ranges, fiber ramp, hydration target) and clarifying it composes on top of chronic conditions. It does not contradict 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, well-structured sentence that front-loads the core purpose and then lists specific output components. Every phrase adds value, with no padding or repetition. It is concise yet informative.

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 carries the burden of explaining expected returns, and it does so by listing specific deliverables (protein floor, fiber ramp schedule, etc.). However, it is vague about how condition and user_context influence results, and it does not mention potential limitations or caveats. Overall, it provides enough context for basic understanding.

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 for parameter meanings. It hints at 'phase' and 'chronic condition' but does not explain the enums for glp1_phase or glp1_medication, nor the nested user_context object. The description focuses on outputs rather than inputs, leaving parameters largely unexplained.

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 a GLP-aware nutrition protocol, listing specific output components (protein floor, fiber ramp, GI interventions, etc.). This distinguishes it from siblings like get_protocol (generic) and glp_phase_guide (phase-focused) by emphasizing a comprehensive, condition-aware protocol.

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 GLP-1 users with active chronic conditions and mentions phase-specific guidance, giving clear context. However, it does not explicitly state when to use this tool over alternatives like glp_phase_guide or query_nutrition_topic, nor does it mention exclusions.

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