get_component_narrative

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

No annotations are provided, so the description carries full burden. It describes the output format and content but fails to mention potential errors (e.g., component not found), side effects (none), or any behavioral traits beyond the return value.

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 that immediately convey the core purpose and content of the output. Every sentence adds essential information; there is no fluff or wasted words.

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 specifies output length and content categories. Minor gap: does not mention error handling or behavior when tagName does not exist, but this is a simple read operation.

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% with both parameters already described in the input schema. The description does not add extra meaning or context for the parameters, so baseline score of 3 is appropriate.

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 returns a 3-5 paragraph markdown prose description covering what a component is, when to use it, customization, slots, and events. This is distinct from sibling tools like get_component (structured data) and get_component_quick_ref (shorter ref), so it effectively differentiates.

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 implies usage for LLM comprehension and high-level understanding, but does not explicitly state when to use it versus alternatives like get_component or list_components. No when-not-to-use guidance is provided.

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