Render an SVG diagram of the human body with muscles highlighted in arbitrary colors. Returns SVG.
| Name | Required | Description | Default |
|---|---|---|---|
| view | No | ||
| gender | No | ||
| layers | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| svg | No | ||
| format | No | ||
| muscles_rendered | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already disclose readOnlyHint=false, idempotentHint=false, and destructiveHint=false, so the agent knows this is not a safe read-only operation. The description adds 'Returns SVG', which clarifies the output format, but does not disclose other behavioral traits such as permissions, side effects, or rate limits. Given annotations exist, this is adequate but not exceptional.
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 both concise and front-loaded. It immediately states the primary action and output, with no wasted words or redundant information. It earns its place by being succinct 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?
Despite the tool's moderate complexity, the description fails to cover essential usage context. It lacks parameter explanations, usage scenarios, and any distinction from sibling tools. The output schema exists, so the return format is partially covered, but the complete lack of parameter semantics and usage guidelines makes the description insufficient 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?
With 0% schema description coverage, the description must compensate by explaining parameters. It hints at 'arbitrary colors' which likely relate to the 'layers' parameter, but it does not explain what 'view', 'gender', or 'layers' should contain. This leaves the required 'layers' parameter ambiguous, offering minimal guidance.
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 action ('Render an SVG diagram') and the resource ('of the human body with muscles highlighted in arbitrary colors'). This distinguishes it from sibling tools like list_muscles or get_muscle_recovery, which deal with data rather than visual generation. The explicit mention of 'Returns SVG' also clarifies the output type.
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 provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or alternative tools. Although 'Render' implies usage for creating images, there is no explicit context or comparison with siblings, making this a gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes (meals, workouts, routines, body metrics, etc.). A few pairs like get_progress and get_muscle_recovery overlap in data but differ in usage, and the AI tools are separated by input type. Overall, an agent can reliably select the right tool.
Tool names predominantly follow a verb_noun snake_case pattern (get_, log_, update_, delete_, search_, list_). The ai_* prefix is consistent but includes noun-like names (ai_meal_plan, ai_photo_macros) that deviate slightly from verb-first convention. Still predictable and readable.
With 36 tools, the server exceeds the typical 'heavy' threshold, but the scope is broad covering meals, workouts, routines, metrics, AI features, and data sync. Each tool serves a distinct capability, so the count is justified though on the higher end.
The server provides solid lifecycle coverage for core resources: meals (create/read/update/delete), workouts (log/get/delete, set updates), body metrics (get/log with profile upsert), routines (list/get/instantiate), and exercise lookup (search/resolve/list). Minor gaps exist for updating/deleting cardio and water entries, but they are not critical.