vital_get_user_connected_providersList the wearables/data sources (providers) a user has connected. Vital API: GET /v2/user/providers/{user_id}.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | Yes | The Vital user id (UUID). |
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?
Annotations already mark readOnlyHint=true, and the description matches with 'List' and 'GET', so there is no contradiction. It adds the endpoint but does not disclose response shape, pagination, or authentication behavior; with the read-only annotation covering the main risk, this is adequate but not rich.
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?
A single sentence front-loads the core action and then appends the exact API path. There are no unnecessary words, and the endpoint information is useful rather than filler.
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 one-parameter, read-only tool, the description is mostly sufficient: it identifies the resource, the user scope, and the required identifier. Missing an explicit statement of the return payload is a minor gap given the simplicity and the lack of an output schema.
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%: user_id is already described as 'The Vital user id (UUID)'. The description adds no parameter semantics, but per the high coverage baseline, this is acceptable.
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 uses a concrete verb ('List') and a specific resource ('wearables/data sources (providers) a user has connected'), and the API path reinforces the per-user scope. It is distinct from vital_list_providers because of the 'has connected' qualifier, though it does not name that sibling explicitly.
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 explicit when-to-use or alternative routing is given. The phrase 'a user has connected' and the GET endpoint imply the intended use case, but an agent must infer how this differs from vital_list_providers or vital_get_user.
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.