Setup GitHub Codespaces

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

The description adds behavioral context beyond annotations: it explains tech stack detection with fallback to DESIGN.md, and that it returns instructions rather than directly creating the Codespace. Annotations already indicate idempotent and non-destructive behavior, so the description's additional details are valuable.

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

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

The description is two sentences, front-loaded with the key action, and every sentence adds essential information without redundancy. It is concise and well-structured.

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

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

Given no output schema, the description adequately explains the return payload (devcontainer.json, routing instructions, setup steps) and covers input parameters implicitly. It provides sufficient context for an AI agent to use the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that extensions are auto-detected if omitted, which clarifies parameter behavior beyond the schema definitions.

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

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

The description clearly states the tool's purpose: detecting the project tech stack and generating a devcontainer configuration for GitHub Codespaces. It specifies the output (devcontainer.json, routing instructions, and setup steps), which distinguishes it from sibling tools like sdd_generate_devcontainer.

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

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

The description mentions that the official GitHub MCP does not expose Codespace creation, implying this tool fills a gap. However, it does not explicitly provide guidance on when to use this tool versus alternatives, nor does it state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.