semantic_search

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

Since no annotations are provided, the description carries full burden. It discloses prerequisite indexing steps and the conditional behavioral difference between whitening-trained fallback and raw nomic, which is good transparency for a read-only search tool.

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 concise sentences, front-loaded with the main purpose. The second sentence contains technical detail that, while valuable, could be daunting for some agents. Overall efficient with no 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 the presence of an output schema, the description does not need to explain return values. It covers the core behavior, prerequisites, and algorithmic fallback. Missing is the role of 'chat_name' and the effect of 'limit', but these are minor gaps.

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 0% and the description adds no parameter-level details. It does not explain 'query', 'limit', or 'chat_name' beyond the schema defaults and types, leaving the agent to guess their semantics.

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 it searches messages by meaning using embeddings, not exact words. It explicitly distinguishes from sibling 'search_messages' (exact match) by contrasting semantic vs. exact search.

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 clear context: use for semantic search when exact word search is insufficient. It mentions prerequisites (embed_index) and fallback behavior, but does not explicitly name the sibling tool for exact search as an alternative.

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