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Glama

team_status

Shows the team roster grouped by working, here, and out—including each member's work, model, and location—so you can check availability before picking up work or choosing a handoff.

Instructions

The team roster grouped by working / here / out: who is on the team, what each is working on, their model, and where. Check it before picking up work or choosing who to hand off to.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "team_status"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.1

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It discloses the return structure and the grouping categories, which implies a read-only snapshot, but never states that it is non-mutating, how fresh the status data is, or whether absent/offline teammates appear. For a zero-parameter read this is adequate but leaves real gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler: the first front-loads what is returned, the second front-loads when to call it. Every clause earns its place and nothing is repeated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must sketch the return value, and it does so with the three status groups and the per-person fields. Combined with a zero-parameter schema, an agent has enough to call it correctly; only freshness and read-only guarantees are unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is no per-argument meaning to convey and the baseline is 4. The description correctly avoids inventing filtering options that the schema does not support.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific resource (the team roster) and its shape, grouped into working / here / out, plus the fields returned: current task, model, and location. It is clear what the tool produces, though it never explicitly distinguishes itself from close siblings like team_members or team_availability, which an agent must infer.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

"Check it before picking up work or choosing who to hand off to" gives a concrete trigger and a decision it supports, which is stronger than most. It stops short of naming alternatives or exclusions (e.g. when to prefer team_availability or team_members instead), so the routing is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.