expand_entity

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

With no annotations provided, the description carries the full burden. It discloses that relations come from GLiNER2 at a 0.5 threshold, that they are sparse, and that a single passage link is 'une piste, pas un fait' — advising to read the passage before asserting. It also explains the direction indicators (→/←) and the co-mention weighting by rarity. It does not explicitly state that the operation is read-only, but that is strongly implied by its nature as a query tool.

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 structured with a main paragraph covering purpose and behavior, followed by a clear 'Args' section. It is somewhat verbose but every sentence adds value: examples, relation semantics, co-mention explanation, and parameter details. The core purpose is front-loaded in the first sentence, making it easy for an agent to grasp quickly.

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 has 4 parameters, an output schema exists (per context signals), and no annotations, the description covers all necessary aspects: input semantics, output structure (neighbors with relation, direction, passage count, chunk_ids), and behavior nuances (sparse relations, co-mentions). It does not detail error cases or edge conditions, but for a read-oriented graph exploration tool, this is sufficient.

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?

The input schema has 0% description coverage, so the description must fully explain each parameter. It does: entity_name includes examples and notes that variants are grouped; relation lists the allowed values and explains that empty returns all; entity_type is described as a disambiguation aid; limit is explained as neighbor count with default 20 and sorting by supporting passages. This completely compensates for the missing schema descriptions.

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 purpose: returns entities related to a given entity in the graph, along with the relation and supporting passages. It provides concrete example questions ('quelles méthodes dépendent de la rough volatility ?') and explicitly describes the output structure. This is a specific verb-resource-object statement that distinguishes it from sibling tools like search_graph or connect_entities.

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 gives explicit context on when to use the tool via example questions and explains the difference between the default behavior (all relations) and the co-mention view when relation is omitted. It does not name alternative tools explicitly, but the examples and the note that co-mentions are 'la vue la plus utile pour explorer un sujet' clearly signal usage scenarios. It lacks an explicit 'when not to use' but is strong on context.

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