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aadityasinghal7

MCP Azure DevOps Server

get_team_members

Retrieve team membership rosters to identify members, administrators, and team composition within Azure DevOps projects.

Instructions

    Retrieves the membership roster for a specific team.
    
    Use this tool when you need to:
    - See who belongs to a particular team
    - Find team administrators
    - Check user assignments across teams
    - Determine team size and composition
    
    Args:
        project_id: The name or ID (GUID) of the team project the team 
            belongs to
        team_id: The name or ID (GUID) of the team
        top: Maximum number of members to return
        skip: Number of members to skip
            
    Returns:
        Formatted string containing team members information including
        display names, emails, IDs, and administrator status, formatted
        as markdown with each member clearly separated
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
team_idYes
topNo
skipNo
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's function and output format (markdown with specific fields), but lacks details on potential limitations like rate limits, authentication requirements, or error handling. However, it provides sufficient context for basic usage.

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 well-structured with clear sections (purpose, usage guidelines, args, returns), front-loaded with the core purpose, and every sentence adds value without redundancy. It efficiently communicates necessary information in a readable format.

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

Completeness5/5

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

For a read-only tool with no output schema, the description provides comprehensive context: clear purpose, usage guidelines, parameter semantics, and detailed return format. It addresses all necessary aspects for an agent to correctly invoke and interpret results, given the tool's complexity and lack of structured annotations.

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

Parameters5/5

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

Given 0% schema description coverage, the description fully compensates by explaining all four parameters in detail: 'project_id' and 'team_id' as required identifiers, and 'top' and 'skip' for pagination control. This adds essential meaning beyond the bare schema, making parameter usage clear.

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

Purpose5/5

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

The description clearly states the specific action ('Retrieves the membership roster') and resource ('for a specific team'), distinguishing it from sibling tools like 'get_all_teams' which lists teams rather than members. The purpose is precise and immediately understandable.

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

Usage Guidelines5/5

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

The description provides explicit bullet points detailing when to use this tool ('See who belongs to a particular team', 'Find team administrators', etc.), giving clear context for application. It effectively guides the agent on appropriate scenarios without ambiguity.

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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