get_embedding_neighbors

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

With no annotations provided, the description carries the full burden of behavioral disclosure, but it only restates the core operation. It does not explain what the returned neighbors are, whether distances are included, how threshold interacts with k, or any assumptions about embedding normalization.

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 short sentences with the core action front-loaded. It is appropriately sized for a simple tool and contains no filler or redundant restatement of the tool name.

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 and no annotations, the description should explain what the tool returns and how the parameters affect results. It does not describe the output shape, whether neighbors are notes or raw vectors, or how k and threshold interact, leaving significant gaps for an agent.

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 100%, so the baseline is 3. The description adds no additional parameter semantics beyond what the schema already provides; 'embedding_vector' is already described as a 384-dimensional array, and k/threshold have defaults and bounds in 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?

The description clearly states the tool's function: 'Find nearest neighbors for a given embedding vector.' This is a specific verb-resource pairing. It does not explicitly contrast with sibling tools like get_similar_notes, though 'custom similarity searches' hints at its distinct vector-based use case.

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 phrase 'Useful for custom similarity searches' provides implied usage context but no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives such as get_similar_notes for note-level similarity or explain when this lower-level vector search would be preferred.

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