find_related

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

With no annotations provided, the description carries the full burden and performs well: it reveals the embedding-based mechanism, notes independence from the link graph, and implies read-only behavior. It stops short of error handling or edge cases, but the available detail is genuinely transparent.

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: a precise technical summary, followed by contextual guidance, then a concise Args section. Every sentence adds distinct value with no redundancy or filler.

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?

For a two-parameter tool with an output schema and thorough sibling differentiation, this is essentially complete. It covers purpose, mechanism, when to use, alternatives, and parameter semantics in under 100 words.

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?

Despite zero schema coverage, the description fully compensates: it clarifies `path` is 'Vault-relative', and enriches `limit` with 'maximum results,' a default of 10, and a 'hard cap 50' that is absent from the schema. Everything beyond the structured fields is useful.

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 finds 'semantically similar notes' using averaged chunk embeddings queried via pgvector. It explicitly distinguishes itself from siblings by naming `get_neighborhood` for link-based exploration and `semantic_search` for arbitrary topic queries.

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 explicit when-to-use guidance: 'useful when the source is sparsely linked or when looking for thematic neighbors.' It also names two concrete alternatives with the conditions under which each should be used instead.

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