GLP Companion Nutrition
Server Details
GLP-1 phase-aware nutrition: muscle preservation, GI tolerance, taper protocols.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.5/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one provides a phase-specific overview, the other generates a full nutrition protocol. No overlap in functionality.
Both tool names use snake_case with the 'glp_' prefix and follow a noun_noun pattern, ensuring consistent and predictable naming.
With only 2 tools, the server feels thin for a 'Companion Nutrition' domain. While the tools cover core informational needs, a broader set (e.g., logging, adjustments) would be more appropriate.
The server lacks tools for ongoing companion features such as meal tracking, symptom logging, or protocol adjustments. It only provides static informational outputs.
Available Tools
2 toolsglp_phase_guideBRead-onlyInspect
Return the protocol overview for a specific GLP-1 therapy phase (starting, titrating, maintenance, tapering, post_drug).
| Name | Required | Description | Default |
|---|---|---|---|
| glp1_phase | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only (readOnlyHint=true) and closed world (openWorldHint=false). The description adds that it returns a protocol overview for a specific phase, but does not elaborate on response format, pagination, or any side effects. The description is consistent 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 extraneous words. It efficiently conveys the tool's purpose.
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 tool with one parameter and no output schema, the description is adequate but vague. 'Protocol overview' lacks detail on what the returned data contains, which could lead to incomplete agent 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?
With 0% schema description coverage, the description compensates by listing the enum values (starting, titrating, etc.) in parentheses. However, it does not describe what each phase entails or provide any additional semantics beyond the enum list.
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, with the phases enumerated. However, it does not explicitly differentiate from the sibling tool 'glp_protocol', leaving the agent to infer the relationship.
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 'glp_protocol' or any other context. The description does not specify prerequisites, limitations, or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
glp_protocolARead-onlyInspect
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, and the description aligns by stating it 'Returns' a protocol. The description adds context by mentioning outputs and compatibility with chronic conditions, complementing annotations without 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 sentence that is front-loaded with the primary action and lists key outputs. While concise, it could be slightly improved by breaking into separate sentences for readability.
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 is complex (GLP protocol, chronic condition, multiple outputs) and lacks an output schema. The description lists outputs but does not explain input dependencies (e.g., how condition affects outputs, or the role of user_context). The context signals show nested objects and enums, but the description does not cover these nuances.
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 4 parameters (condition, glp1_phase, user_context, glp1_medication) with 0% description coverage. The tool description does not explain any parameter's meaning, format, or constraints, relying entirely on schema names which may be ambiguous (e.g., user_context as nested object).
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 it generates a GLP-aware nutrition protocol and lists specific outputs (protein floor, fiber ramp schedule, etc.), distinguishing it from the sibling tool glp_phase_guide which likely focuses on 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 for generating comprehensive protocols for GLP-1 medication users with chronic conditions, but does not explicitly state when to use vs alternatives or provide when-not-to-use guidance.
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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