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get_team

Retrieve detailed information about a specific team using its unique ID to manage team data and access within the Devici security platform.

Instructions

Get a specific team by ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool handler for 'get_team'. It is decorated with @mcp.tool() which registers it, fetches the team data via the API client, converts to string, and returns it.
    @mcp.tool()
    async def get_team(team_id: str) -> str:
        """Get a specific team by ID"""
        async with create_client_from_env() as client:
            result = await client.get_team(team_id)
            return str(result)
  • Helper method in the Devici API client that makes the HTTP GET request to retrieve the specific team by ID from the Devici API endpoint /teams/{team_id}.
    async def get_team(self, team_id: str) -> Dict[str, Any]:
        """Get specific team by ID."""
        return await self._make_request("GET", f"/teams/{team_id}")
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only states the basic action without mentioning whether this is a read-only operation, what permissions are needed, how errors are handled, or what the response format entails. This is inadequate for a tool with potential security or operational implications.

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?

The description is extremely concise—a single sentence that directly states the tool's purpose without any fluff. It's front-loaded and wastes no words, making it easy to parse quickly.

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

Completeness3/5

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

Given the tool's low complexity (single parameter, read-like operation) and the presence of an output schema (which handles return values), the description is minimally complete. However, it lacks context about team ID sourcing, error handling, or integration with sibling tools, leaving gaps for an agent to infer usage.

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

Parameters3/5

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

The description adds minimal semantics beyond the input schema: it clarifies that 'team_id' identifies a specific team. However, with 0% schema description coverage and only one parameter, this provides basic but insufficient context (e.g., no format examples or validation rules). The baseline is appropriate given the simple parameter structure.

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 clearly states the verb ('Get') and resource ('a specific team by ID'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'get_teams' (which presumably lists multiple teams), so it misses full differentiation.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'get_teams' or other sibling tools. It lacks context about prerequisites, error conditions, or typical use cases, leaving the agent with minimal usage direction.

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

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