Generate a GLP-aware nutrition protocol composed on top of any active chronic condition. Returns protein floor (1.2-1.6 g/kg), fiber ramp schedule, GI tolerance interventions, resistance training prescription, hydration target, and phase-specific guidance.
| Name | Required | Description | Default |
|---|---|---|---|
| condition | No | ||
| glp1_phase | Yes | ||
| user_context | No | ||
| glp1_medication | Yes |
Changes observed during successful MCP inspections.
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.
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