why

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

No annotations are provided, so the description carries full burden. It transparently describes the tool's behavior: finding the best-matching memory optionally scoped by a filter, and returning a connected subgraph through typed links with configurable max_hops. It does not disclose potential side effects or permissions, but for a read-like tool this is acceptable.

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 a single, well-structured sentence that front-loads the purpose and efficiently conveys the tool's unique value proposition without 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 the tool's complexity (graph traversal, fusing search methods) and the presence of an output schema, the description is fairly complete. It explains the process and result, though it could elaborate on the nature of 'typed links' or the subgraph structure.

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 coverage is 100%, so the baseline is 3. The description mentions optional filter scoping and max_hops implicitly via 'reachable through typed links', but does not add significant new meaning beyond what the schema already provides for the parameters.

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 explains a decision by finding a best-matching memory and returning a connected subgraph of related memories via typed links. It distinguishes itself from siblings like 'recall' by highlighting the fusion of vector, ColumnStore, and graph search to surface context beyond plain similarity.

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 implies usage when a plain similarity search is insufficient, providing context for when the tool is most valuable. However, it does not explicitly state when not to use it or directly compare with sibling tools like 'recall_where' or 'recall_fused'.

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