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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds useful return context ('most relevant passages with their page URLs') but says nothing about result count, ranking behavior, or empty-result behavior.

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?

Two sentences, zero waste, with the action front-loaded and the return shape following it. Nothing is repeated from the title or schema.

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?

For a single-parameter read tool with full annotation coverage and an inline schema example, this is nearly complete: the action and the shape of the response are both stated. The only real gap is the absence of routing guidance against its many siblings.

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% and includes a concrete example, so the schema does the heavy lifting. The description only reinforces that the query is natural language; it adds no format, length, or scoping guidance beyond what the schema provides.

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?

States a specific verb and resource ('Search the Liliphant API documentation') plus the input mode (natural-language question). It is clearly distinguishable from get_liliphant_api_docs in spirit, but never names the siblings it differs from, so differentiation is left to inference.

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?

Implies usage through 'natural-language question,' which suggests this is the semantic-search path rather than a direct doc fetch. However, it never states when to pick this over get_liliphant_api_docs, ask_a_question, or call_any_api_of_liliphant, so the agent must guess at the routing.

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