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wuqunfei

github-mcp-server-js

by wuqunfei

create_codespace_in_repo

Create a cloud-based development environment for a GitHub repository, with options for branch, machine type, and dev container configuration.

Instructions

Create a codespace in a repository for the authenticated user. Docs: https://docs.github.com/en/rest/codespaces/codespaces#create-a-codespace-in-a-repository

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refNoGit ref (branch/tag/SHA) to base the codespace on.
repoYesRepository name
ownerYesRepository owner (user or organization login)
machineNoMachine type (e.g. "basicLinux32gb").
locationNoPreferred Azure region (e.g. "WestUs2").
display_nameNoHuman-readable name for the codespace.
devcontainer_pathNoPath to devcontainer.json inside the repo.
working_directoryNoWorking directory inside the codespace.
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action 'Create a codespace' without covering permissions, side effects, return values, or async behavior. The docs link offers external details, but the description text itself lacks substantive behavioral transparency.

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 a single concise sentence with a docs link, immediately stating the core purpose. There is no wasted text, and the structure is front-loaded and clear.

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

Completeness2/5

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

Given the tool's complexity (8 parameters, no output schema, no annotations), the description is too minimal. It does not explain what the created codespace looks like, what is returned, or any prerequisites beyond being authenticated. The docs link mitigates this somewhat, but the description alone is insufficient for an AI agent to fully understand the tool's behavior.

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 input schema provides descriptions for all 8 parameters, giving 100% coverage. The description adds no extra parameter meaning, but with full schema coverage, a baseline of 3 is appropriate.

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 action ('Create a codespace in a repository') and the context ('for the authenticated user'), using a specific verb and resource. This distinguishes it from sibling tools like list_codespaces, get_codespace, start_codespace, and stop_codespace.

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?

The description provides clear context for when to use the tool—when you want to create a codespace in a repository for the authenticated user. It does not explicitly mention alternatives or exclusions, but the intended use case is unambiguous given the tool name and description.

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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