GLP Companion Nutrition

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

Annotations declare readOnlyHint=true and openWorldHint=false, so the safe-read nature is already known. The description simply repeats 'Return' and does not add extra behavioral context such as return format, error handling, or what 'protocol overview' includes. No contradiction with 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, front-loaded sentence with no redundant words. It efficiently conveys the tool's purpose and includes the parameter values in a parenthetical, making it highly scannable.

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?

For a simple one-parameter read-only tool with annotations and no output schema, the description is adequate. It omits return format details, but the straightforward nature of the tool makes this acceptable. Could be improved by specifying the output type or content.

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 input schema has zero parameter descriptions, so the description must compensate. It does so by enumerating the valid phase values (starting, titrating, maintenance, tapering, post_drug), matching the schema enum. However, it doesn't explain the meaning of each phase or how the parameter affects the output, so it doesn't fully compensate.

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 the protocol overview for a specific GLP-1 therapy phase and lists all five valid phases. This provides a specific verb, resource, and scope, distinguishing it from the sibling glp_protocol which likely covers the full 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 this tool is for phase-specific overviews but gives no explicit guidance on when to use it versus glp_protocol, nor any exclusions or prerequisites. The usage context is understandable but not clearly demarcated.

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 include readOnlyHint=true, so the tool is known to be a safe read operation. The description adds the return components, which is useful, but it does not disclose any additional behavioral traits such as how conditions are handled, limitations, or what happens with incomplete inputs. No contradiction with 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, dense sentence packed with meaningful output details. It is front-loaded with the primary verb and resource, and every phrase adds value without being verbose.

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?

The tool has four parameters, nested objects, and no output schema. The description lists the output components, which is helpful, but it does not explain how parameters influence the protocol, what the output structure looks like, or any usage constraints. This leaves significant gaps for an agent to invoke the tool correctly.

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 description does not explain any of the four parameters. It mentions 'phase-specific guidance' which hints at glp1_phase and 'any active chronic condition' which relates to condition, but it does not clarify how glp1_medication or user_context affect the output. Schema coverage is 0%, so the description carries full responsibility but fails to compensate.

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's action ('Generate') and resource ('GLP-aware nutrition protocol'), and lists specific outputs (protein floor, fiber ramp schedule, etc.). It implicitly distinguishes itself from the sibling tool glp_phase_guide by focusing on a complete nutrition protocol rather than just phase guidance.

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 context ('composed on top of any active chronic condition', 'phase-specific guidance') but does not explicitly state when to prefer this tool over glp_phase_guide or when it should not be used. No exclusions or alternatives are mentioned.

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