cloudwright-mcp

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it 'Calls an LLM provider — incurs API costs' (cost implication), is a 'Pure function: returns a new spec without mutating the input' (non-destructive and idempotent), and 'Does not deploy' (scope limitation). This covers critical behavioral aspects beyond basic functionality.

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 front-loaded with the core purpose, followed by clear sections for 'When to use' and 'Behavior'. Each sentence adds value—distinguishing from siblings, explaining costs, and clarifying functional behavior—with zero wasted words. The structure is logical and efficient.

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 annotations and no output schema, the description does well to cover key behavioral aspects like costs, non-mutation, and non-deployment. However, it doesn't detail the return format (e.g., what the updated ArchSpec looks like) or error handling, leaving some gaps for a tool with complex input/output. It's largely complete but could be slightly enhanced.

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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds some context by mentioning 'natural-language instruction' and that the LLM interprets it, but this largely reiterates what's in the schema (e.g., 'Plain-English modification instruction'). It doesn't provide significant additional semantic value beyond the well-documented schema.

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 with specific verbs ('modify an existing architecture', 'evolve it') and resources ('ArchSpec'), and distinguishes it from sibling tools like 'design_architecture' for from-scratch design and 'chat_create_session' for iterative editing. It explicitly names the alternative tools, making the purpose unambiguous.

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 provides explicit guidance on when to use this tool ('You already have an ArchSpec and want to evolve it') and when not to use it (for 'from-scratch design' use 'design_architecture', for 'iterative multi-turn editing with conversation memory' use 'chat_create_session'). It clearly names the alternatives and specifies the prerequisite of having an existing ArchSpec.

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