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backlog_get_users

Retrieve project members from Backlog with their IDs, names, emails, and roles. Use the numeric ID to assign issues or filter by assignee.

Instructions

Fetch project members for a given Backlog project.

Returns a table of users with their numeric ID, userId, display name, email, and role. Use the ID column as assigneeId in backlog_get_issue_list to filter by assignee.

INPUT:

  • projectIdOrKey (required): project key e.g. "MYPROJ" or numeric ID e.g. "12345"

  • keyword (optional): filter by display name or userId, case-insensitive

EXAMPLE: List all members of project "MYPROJ" → { projectIdOrKey: "MYPROJ" } EXAMPLE: Find user named "Nguyen" → { projectIdOrKey: "MYPROJ", keyword: "nguyen" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdOrKeyYesProject key or numeric ID. Examples: "MYPROJ", "12345". Use backlog_get_projects to discover project keys.
keywordNoFilter by display name or userId (case-insensitive). Example: "nguyen" or "john.doe"
Behavior4/5

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

No annotations are provided, so the description carries the burden. It describes the input parameters and gives examples. The read-only nature is implied but not stated explicitly. However, it does not contradict any annotations.

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 concise, well-structured with clear sections (INPUT, EXAMPLE), and uses bullet points. Every sentence adds value without redundancy.

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?

Despite lacking an output schema, the description details the return fields and demonstrates usage. It provides sufficient context for an AI agent to understand the tool's purpose and behavior.

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?

With 100% schema coverage, the description adds value by providing examples, filtering behavior (case-insensitive), and how to use the output. This goes beyond the schema's parameter descriptions.

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 it fetches project members for a Backlog project, specifies the output fields (ID, userId, display name, email, role), and distinguishes itself from sibling tools by focusing on user/member retrieval.

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?

It provides clear context on when to use the tool (to get project members) and how to use the output (ID as assigneeId in backlog_get_issue_list). It does not explicitly exclude alternatives, but the purpose is well-defined.

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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