list_active_meetingsList a team's currently-active (in-progress) meetings. Parabol GraphQL: viewer.team(teamId).activeMeetings. Requires TEAMS_READ + MEETINGS_READ.
| Name | Required | Description | Default |
|---|---|---|---|
| teamId | Yes | The team id. |
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 declare readOnlyHint=true, so the safe-read nature is established. The description adds useful behavioral context by naming required permissions (TEAMS_READ + MEETINGS_READ) and the GraphQL endpoint. It doesn't describe return shape or pagination, but the annotation lowers the burden on the description.
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 three short, purposeful statements: what it does, the underlying GraphQL query, and required permissions. No filler words; the core action is front-loaded.
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, single-parameter list operation, the description covers purpose, operation source, and permissions. There is no output schema, but the description is sufficient for an agent to know when and how to call this tool 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?
Schema description coverage is 100%, with the single parameter teamId documented as 'The team id.' The description doesn't add extra meaning to the parameter beyond what the schema provides, which aligns with the baseline for fully covered schemas.
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 specific verb and resource: 'List a team's currently-active (in-progress) meetings.' It distinguishes this from sibling tools like list_meetings and get_meeting by explicitly limiting scope to active meetings. The embedded GraphQL path further clarifies exactly what operation is performed.
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 gives clear context for when to use the tool: when a team's currently-active meetings are needed. It doesn't explicitly name alternatives or exclusion conditions, but the 'currently-active (in-progress)' qualifier makes the use case unambiguous relative to siblings like list_meetings.
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.