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matt-nann

bitbucket-mcp

by matt-nann

bb_list_workspace_members

List known workspace members to resolve a person's name to their UUID. Members come from a local directory as the single source of truth.

Instructions

List known workspace members to resolve a person's name to their UUID.

Members come from the local known-members directory (see directory.py). Bitbucket's live members API is not used: it requires the account scope the MCP token doesn't carry, so it only ever returned 403. The directory is the single source of truth — seed people into directory.json or the BITBUCKET_KNOWN_MEMBERS env var.

You usually don't need to call this before bb_add_pull_request_reviewers — that tool resolves names against the same directory itself. Use this to inspect who is known, or when a name is ambiguous. If more than one member matches, present the candidates rather than guessing.

Args: query: Optional case-insensitive name filter (display name or nickname). workspace: Bitbucket workspace slug.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoOptional case-insensitive name filter matched against each member's display name and nickname. Omit to list everyone.
workspaceNoBitbucket workspace slug. Defaults to BITBUCKET_WORKSPACE.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations provided, but the description fully compensates by explaining why the live API is not used (403 due to scope), the directory as source of truth, and how to seed members.

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

Conciseness4/5

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

Well-structured with a clear purpose sentence, explanatory paragraphs, and parameter details. Compact but not terse; could be slightly more concise but effective.

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 simple two-parameter tool with no enums and an output schema, the description covers purpose, internals, usage guidance, and parameter semantics comprehensively.

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?

Schema coverage is 100%, and the description adds useful context like case-insensitivity and nickname matching for query, and default workspace behavior, going slightly beyond the schema.

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 tool lists known workspace members to resolve a person's name to their UUID, with a specific verb and resource. It distinguishes from sibling tools that focus on pull requests.

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?

Explicitly advises when to use (inspect known members, handle ambiguity) and when not to (usually not needed before bb_add_pull_request_reviewers), and provides guidance on ambiguous matches.

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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