vital_get_bodyGet body summaries for a user over a date range. Vital API: GET /v2/summary/body/{user_id}.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | Yes | The Vital user id (UUID). | |
| end_date | No | End date yyyy-mm-dd (inclusive). Defaults to today upstream. | |
| provider | No | Filter to a single provider slug (e.g. oura, fitbit). | |
| start_date | Yes | Start date yyyy-mm-dd (inclusive, required). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
readOnlyHint=true already establishes the safety profile, and the description adds the exact GET endpoint and summary scope. However, it does not disclose response shape, pagination, or other runtime behavior; the annotation lowers the burden, so a 3 is appropriate.
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?
Two focused sentences with no redundancy. The main action and scope are front-loaded, followed by the precise REST endpoint. Every part earns its place.
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 request side is adequately covered: user, date range, provider, and endpoint are identifiable. But with no output schema, the description does not explain what a 'body summary' contains or how results are returned, leaving a noticeable completeness gap.
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 coverage is 100%, so the baseline of 3 applies. All parameters already have descriptions; the description's 'date range' wording merely reinforces start_date and end_date without adding meaning beyond the schema.
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 says exactly what the tool does: 'Get body summaries for a user over a date range.' It names a concrete resource (body summaries) and the exact Vital API endpoint, clearly distinguishing it from sibling getters like vital_get_activity or vital_get_sleep.
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?
No guidance is given about when to use this tool versus alternatives such as vital_get_timeseries or other summary endpoints. The date-range phrasing implies usage, but there are no explicit when-to-use or when-not-to-use conditions.
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.
Each tool maps to a unique resource/action pairing—users, health summaries, timeseries, providers, and lab orders—so an agent can reliably distinguish them. Even similarly named getters are separated by the data domain (activity/body/sleep/workouts) and description.
All tools use the vital_ prefix and snake_case verb_noun forms, which is highly predictable. Minor inconsistency: get is used for both single-resource fetches and list-returning calls (get_workouts, get_user_connected_providers) while list is reserved for global collections.
21 tools is on the heavier side, but the breadth of the Vital API—users, providers, many health summary types, timeseries, and lab tests/orders—justifies most of them. It is slightly over a typical focused MCP server but not bloated or redundant.
The read side is strong: users, providers, summaries, timeseries, lab tests, and results are all covered. However, there are no update/delete user operations and no way to create a lab-test order, so core lifecycle/workflow gaps remain.