tigergraph__search_top_k_similarity

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

Since no annotations are provided, the description must carry transparency. It describes the return (top-K with distances) and implies a read-only operation through the term 'search', but does not explicitly state the absence of side effects or any permission requirements. This is adequate but not exhaustive.

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 concise and well-structured: a main statement, an important note, and a related tools list. No fluff or 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 no output schema, the description adequately explains the return (top-K vertices with distance scores). It also mentions related tools for dimension checking and index status, providing sufficient context for usage. No critical information is missing.

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 descriptions cover all parameters (100% coverage). The description adds an important note about query_vector dimension matching, which supplements the schema. This extra context is valuable and goes beyond the 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?

Clearly states that the tool performs vector similarity search using TigerGraph's vectorSearch() function and returns top-K similar vertices with distance scores. This is specific and distinguishes it from sibling tools like fetch_vector or list_vector_attributes.

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?

Provides guidance to check the expected dimension using list_vector_attributes before searching, which is a helpful precondition. However, it does not explicitly enumerate alternatives or state when to prefer this tool over other vector-related operations, though the purpose makes it obvious.

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