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

Beyond annotations that declare read-only and idempotent behavior, the description reveals non-obvious traits: it renders an interactive widget, operates client-side with zero PII and zero network, and uses AIN Bridge for inputs. This fully discloses the execution model and data privacy.

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 with a clear front-loaded action. The second sentence is slightly long, bundling multiple details, but each part serves a purpose. Minor verbosity is acceptable given the complexity.

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 (linter with output schema, widget rendering, client-side execution) and rich annotations, the description covers purpose, usage, behavioral traits, input mechanism, and return values (findings, score, annotation set). No gaps are present.

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?

The input schema already provides 100% description coverage for the 'inputs' parameter ('Map of tool input element IDs to values...'). The description adds only minor context about widget rendering and AIN Bridge prefill, not substantially enriching the parameter meaning beyond what the schema offers.

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 validates MCP tool definitions against specific rules (JSON Schema 2020-12, naming, output-schema, annotations) and returns findings. However, it does not explicitly differentiate from sibling lint tools that focus on domain-specific validation, relying on context.

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 says 'Use when a developer wants to check an MCP tool definition before publishing,' providing clear context. It does not mention when to avoid using it (e.g., for domain-specific linting), but the sibling list implies alternative tools exist.

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