connect_entities

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

With no annotations provided, the description carries the full behavioral burden and delivers: it discloses the ranking behavior (documents by number of common passages, then passages), the deduplication constraint (top_k but at most 2 per document), and the empty-result semantic ('Aucun passage commun est une information en soi'). It could add read-only/safety notes, but for an obvious search tool the disclosed behavior is substantive.

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 compact and front-loaded: purpose in the first line, ranking/empty-result semantics in the second, and a clean Args block. The inline example entities ('SVI' and 'rough volatility') are slightly odd since an agent could momentarily read them as fixed values, but the Args block dispels that, and no sentence is wasted.

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 3-parameter tool with an output schema present (so return format is covered elsewhere), the description is nearly complete: it covers purpose, use case, ranking order, the top_k-per-document cap, empty-result semantics, and all parameter meanings. Missing only error/not-found behavior, which is a minor gap for a search tool.

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 0%, and the description fully compensates by documenting all three parameters with meaning beyond the bare type: entity_a as 'nom ou fragment, variantes réunies' (name or fragment with variants grouped), entity_b by analogy, and top_k with its 'default 10, at most 2 per document' effective behavior. This resolves ambiguity the schema alone cannot.

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 opening line states a specific verb+resource: 'Passages et documents qui mentionnent LES DEUX entités' – a two-entity co-occurrence search, clearly distinct from siblings like search_documents (single-entity) and get_passage (single passage retrieval). It also names the search target precisely: documents ranked by common passage count, then the best passages with chunk_id.

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 an explicit use case: 'Pour trouver où deux idées se rencontrent dans le corpus' (to find where two ideas meet in the corpus). This provides clear context for when to invoke the tool, though it does not name alternative tools or state when not to use it – no exclusions are given despite siblings like search_graph existing for similar relational queries.

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