vector_search

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

With no annotations, the description carries full burden. It explains HNSW index usage, ef parameter trade-off, and return format. However, it doesn't mention error behavior (e.g., missing index) or performance nuances beyond ef, leaving minor gaps.

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?

Well-structured with clear sections (purpose, usage guidelines, args, returns, example). Every sentence adds value; no fluff. Front-loaded with core purpose.

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 5 parameters, no annotations, and an output schema (context says true), the description covers functionality, prerequisites, parameters, return format, and example. Lacks only minor details like error conditions or ef formula, but overall strong.

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%, so description must explain parameters fully. It does so for all 5 parameters: label, property, query_vector, k (with default 10), and ef (with default None). Example call demonstrates usage pattern.

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 starts with a clear verb+resource: 'Find the k nearest nodes by vector similarity (HNSW index).' It distinguishes from sibling tools like search_nodes_by_label (keyword search) and execute_gql (general queries), making the purpose unambiguous.

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?

Explicitly states when to use ('when you have an embedding vector and want semantically similar nodes'), when not to use ('Do NOT use for keyword or property search'), and even requires a prerequisite: vector index created via create_vector_index. Example call reinforces correct usage.

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