explain_join

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. It does well by detailing the return behavior: it returns the chosen path as a sequence of hops, and in cases where no single path is preferred, it returns null with an 'ambiguous: true' flag and a note, as well as listing every alternative path and the reason it was not chosen. This gives the agent a clear expectation of outcomes. It does not explicitly state that the tool is read-only, but the verb 'explain' and the nature of the task make that implicit. It also does not mention any side effects, which is appropriate for a query-style 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 well-structured with sections for args and returns, and it is not overly verbose. It front-loads the main purpose in the first sentence. The inclusion of examples and the detailed return behavior is valuable, though it could be slightly trimmed without losing meaning. Overall, it is efficient and earns each sentence's place.

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?

The tool has an output schema, so the return structure is partially defined. The description adds key context beyond that: the meaning of 'ambiguous' and the fact that alternatives are also returned with reasons for non-selection. It does not mention that entity names must correspond to known entities (e.g., from list_entities), but this is derivable from the example and the nature of the tool. Given the moderate complexity (2 params, no nested objects), the description covers what an agent needs to invoke and interpret the tool correctly.

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?

Because schema description coverage is 0%, the description must clarify the parameters, and it does so excellently. It defines 'from_entity' as the starting entity name with a concrete example ('invoices') and 'to_entity' as the destination entity name with an example ('customers'). This goes beyond the bare schema by providing real-world usage examples, making it clear what values are expected and how they are used.

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: to explain the preferred join path between two entities in words, including what it avoided. It names the resource (join path) and the specific verb (explain), and it distinguishes itself from sibling tools like query_metric or describe_entity by focusing on the join path explanation rather than data retrieval or entity metadata. The scope is precise and unambiguous.

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 one needs to understand how two entities relate, but it does not explicitly state when to use this tool versus alternatives. There is no 'use this when...' or 'instead of...' guidance. However, since no sibling tool is directly comparable (list_entities, describe_entity, etc. serve different purposes), the context is clear by implication. It lacks explicit exclusions or alternative routing.

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