vital_get_menstrual_cycleGet menstrual cycle summaries for a user over a date range. Vital API: GET /v2/summary/menstrual_cycle/{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?
The readOnlyHint annotation already covers non-destructiveness, and the description adds the GET endpoint and date-range scope. However, it does not add further behavioral context such as aggregation granularity, potential empty results, or provider-specific behavior.
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 two sentences with no filler. The primary action and endpoint are front-loaded, and every word serves a 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 read-only summary tool with complete schema documentation, the description is mostly sufficient. The absence of an output schema is a minor gap, but the tool's behavior and inputs are reasonably clear.
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 100%, so the input schema already documents all parameters clearly. The description only adds the concept of a date range, which slightly reinforces start_date/end_date but does not provide additional parameter-level meaning.
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 a specific action: retrieving menstrual cycle summaries for a user over a date range. The resource and endpoint are explicit, making it distinguishable from sibling data-retrieval tools like vital_get_activity, vital_get_sleep, and vital_get_workouts.
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 alternatives, and no mention of exclusions or fallback tools. The agent must infer from the resource name and sibling list that this tool is appropriate for menstrual cycle data.
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.