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 and discloses key behavioral traits: it states the tool is 'pure graph computation — no LLM, no network', 'Read-only', and 'Does not touch cloud resources'. This covers safety, computational nature, and resource interaction, though it could add more on performance or limitations like graph size constraints.

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 appropriately sized and front-loaded, starting with the core purpose, followed by usage guidelines and behavior. Every sentence earns its place by adding distinct value—no redundancy or waste, making it efficient 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 the tool's complexity (graph analysis with 2 parameters) and no annotations or output schema, the description is largely complete: it explains purpose, usage, behavior, and outputs. However, it lacks details on return format or error handling, which could be useful for an agent, slightly reducing completeness.

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 minimal value beyond the schema, mentioning 'component_id' for focusing analysis but not elaborating on 'spec_json' beyond what's in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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 ('analyze', 'returns') and resources ('blast radius and dependency structure of an architecture'), detailing outputs like direct dependents and SPOF status. It distinguishes from siblings by contrasting with 'score_architecture' and 'lint_architecture', making the purpose explicit and differentiated.

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 ('You have a spec and want to understand coupling and failure domains before production') and names complementary alternatives ('score_architecture' and 'lint_architecture'). It also specifies usage context ('for each component or just one'), offering clear when-to-use and when-not-to-use scenarios.

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