living_body_agent_manifestGet AI agents onboarded by retrieving machine-readable install and operating instructions that auto-detect wellness connectors and compose them into one body data layer.
Machine-readable install and operating instructions for AI agents. Call first when onboarding.
| Name | Required | Description | Default |
|---|---|---|---|
| client | No | generic | |
| response_format | No | json |
Changes observed during successful MCP inspections.
v0.3.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is safe and non-destructive. The description adds that it returns 'install and operating instructions,' which is useful context but not essential beyond 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 very concise—two sentences with no fluff. It is front-loaded with the core purpose. However, it could be slightly expanded to cover parameter hints without losing concision.
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 tool's simplicity (no required params, no output schema, rich annotations), the description is largely complete. It explains what the tool is and when to use it. The only minor gap is parameter semantics, but overall it covers the essential context.
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%, meaning parameter properties lack descriptions. The description does not elaborate on the parameters (client, response_format). While the enum values are somewhat self-explanatory, the description fails to add meaning for an agent to choose appropriate values.
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 states a clear purpose: 'Machine-readable install and operating instructions for AI agents' with a specific usage directive 'Call first when onboarding.' This distinguishes it from sibling tools like status, health_check, and capabilities.
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 explicitly says 'Call first when onboarding,' providing clear when-to-use guidance. It does not explicitly state when not to use or name alternatives, but given the sibling tool list, the usage context is well implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.