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arclabs-studio

ARCLinearGitHub-MCP

workflow_generate_branch_name

Generate a standardized branch name from a branch type, description, and optional Linear issue ID, following naming conventions for consistency.

Instructions

Generate a valid branch name following naming conventions.

Args: branch_type: Type of branch (feature, bugfix, hotfix, docs, spike, release) description: Short description for the branch issue_id: Optional Linear issue ID (e.g., 'PROJ-123')

Returns: Dictionary with generated branch name

Examples: - branch_type='feature', issue_id='PROJ-123', description='user authentication' -> 'feature/PROJ-123-user-authentication' - branch_type='docs', description='Update README' -> 'docs/update-readme'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
issue_idNo
branch_typeYes
descriptionYes
Behavior3/5

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

With no annotations, the description carries full burden. It explains the tool generates a name and returns a dictionary, but does not disclose whether the name is validated against existing branches, what conventions are applied, or if any external calls are made. The 'valid' claim is vague without referencing conventions.

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 and well-structured with Args, Returns, and Examples sections. Every sentence adds value, and the overall length is appropriate given the complexity.

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?

The tool has no output schema, so the description must specify the return format. It states 'Dictionary with generated branch name' but does not indicate the key name (e.g., 'branch_name'). Additionally, it does not clarify if the tool is purely computational or if it checks remote branches. This leaves some ambiguity for the agent.

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 0%, so the description must compensate. It lists all three parameters with explanations: branch_type with allowed values, description as short description, and issue_id with example format. This adds significant meaning beyond the schema's bare types.

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 generates a valid branch name following naming conventions. The verb 'generate' and resource 'branch name' are specific, and it is distinct from siblings like workflow_validate_branch_name (validation) and github_create_branch (creation).

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

Usage Guidelines3/5

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

The description provides examples but does not explicitly state when to use this tool over alternatives. It implies use when a branch name is needed before creation, but lacks guidance on when not to use it or how it differs from validate/start features.

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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