semantic_search

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

With no annotations provided, the description must disclose behavioral traits. It mentions the use of embeddings and result boosting for stack filters, which adds value. However, it does not cover idempotency, rate limits, authentication, or side effects, leaving gaps in transparency.

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 highly concise, with a clear introductory sentence and a structured Args section. Every sentence provides useful information without redundancy.

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 that an output schema exists, the description does not need to explain return values. It covers purpose, parameters, and behavioral hints (boosting). It could be more complete by specifying the data source being searched, but overall it is well-rounded.

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 schema description coverage is 0%, but the description richly explains each parameter: query as natural language, top_k as result count with default, stack as optional filter with boosting behavior. This adds essential meaning beyond the bare schema.

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 performs semantic search using LanceDB vectors and Gemini embeddings, and distinguishes itself by being 'more intelligent than keyword search.' However, it does not explicitly name alternative sibling tools, leaving some ambiguity for the agent.

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 use for semantic search with a comparison to keyword search, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among sibling tools like 'search_knowledge' or 'episodic_search.'

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