get_viewerFetch the authenticated Parabol user (the token owner): id, preferredName, email, tier, and the team ids (tms) they belong to. Parabol GraphQL: query { viewer { ... } }. Requires the USERS_READ scope. Start here to discover your identity and team ids.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 reinforces this with 'Fetch' and 'authenticated user'. It adds useful context about the token owner and required scope. No contradiction, though null/error behavior is not discussed; the annotation lowers the bar.
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?
Three short sentences: the main purpose and returned fields, the underlying GraphQL shape, and a usage pointer. Every sentence earns its place, and there is no repetition of annotation data or 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 zero-parameter, read-only identity tool with no output schema, the description covers what is returned, the required scope, and the intended starting use case. Nothing essential is missing for an agent to select and invoke it 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?
The input schema has zero parameters, so the baseline is 4. The description correctly adds no parameter detail and instead enumerates the meaningful response fields, which is the relevant semantic content here.
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 opens with a specific verb 'Fetch' and names the exact resource: the authenticated Parabol user (token owner). It enumerates the returned fields (id, preferredName, email, tier, tms), making it easy to distinguish from team, organization, and meeting sibling tools.
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?
'Start here to discover your identity and team ids' provides a clear entry-point context, and the USERS_READ scope states a prerequisite. However, it does not explicitly contrast with the generic parabol_graphql/parabol_query siblings or state when not to use this tool.
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.
Every tool targets a distinct resource and action: get_/list_ tools are clearly separate read paths, create_/update_/start_/add_ tools map to distinct mutations, and the two escape hatches are explicitly labeled. Even near neighbors like list_meetings and list_active_meetings are unambiguous.
Most tools follow a consistent pattern: list_/get_ for reads, create_/update_/start_ for mutations, with plural nouns for collections and singular for single resources. Minor deviations like add_comment, invite_to_team, and the parabol_* escape hatches prevent a perfect score.
With 21 tools, the server sits in the 16-25 range that feels heavy for an MCP surface. The breadth is somewhat justified because Parabol spans organizations, teams, meetings, tasks, and comments, but many tools are list/get variants of the same core entities.
Core read and write flows exist for teams, organizations, meetings, tasks, and comments, but there are notable lifecycle gaps such as no delete_task, no reflection update/delete, and no meeting end/update mutations. The parabol_graphql and parabol_query escape hatches help work around these gaps but do not make the curated surface fully complete.