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 thoroughly explains key behaviors: it 'Calls an LLM provider (Anthropic or OpenAI depending on configured keys) — incurs API costs per invocation,' describes deterministic post-processing layers, specifies safe defaults applied, and clarifies what it does not do ('Does not deploy or modify any cloud resources'). This covers cost, implementation details, and limitations.

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 well-structured with clear sections (primary function, when to use, behavior) and uses bullet-like formatting for readability. Every sentence adds value: the first states the core purpose, the second details the output, the third provides usage guidelines with alternatives, and the fourth explains behavioral traits. There is no wasted text.

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 the tool's complexity (cloud architecture design with LLM integration) and the absence of annotations and output schema, the description does an excellent job covering purpose, usage, and behavior. However, it doesn't detail the return format (ArchSpec structure) or error handling, which could be helpful since there's no output schema. This minor gap prevents a perfect score.

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 all parameters thoroughly. The description does not add any parameter-specific information beyond what the schema provides (e.g., it doesn't explain parameter interactions or usage nuances). According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.

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: 'Design a cloud architecture from a natural-language description' with specific details about what it returns ('complete ArchSpec with components, connections, tier assignments, and a cost estimate'). It distinguishes itself from siblings like 'modify_architecture' and 'chat_create_session' by being the 'Primary entry point for greenfield architecture design.'

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 explicitly states when to use this tool ('When to use: You have a requirement (prose) and need a concrete architecture with services, wiring, and cost') and provides clear alternatives ('Use `modify_architecture` to iterate on an existing spec, or `chat_create_session` + `chat_send` for multi-turn refinement'). This gives precise guidance on tool selection.

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