june_resolve

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

No annotations provided, but description fully discloses behavior: runs server-side, reversible, default conservative, upgrade to semantic on Pro endpoints. Also states what happens with strong_only parameter and return format.

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 concise and well-structured, covering purpose, usage, parameters, and return in a single paragraph. Slightly dense but each sentence adds value.

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 tool with 2 parameters and no output schema, the description provides sufficient context: explains when to run, parameter behavior, and return structure. Minor gap on min_confidence, but overall complete.

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 0%, so description must explain parameters. It explains the 'strong_only' parameter well (default behavior and effect of false), but does not mention the 'min_confidence' parameter at all, leaving ambiguity.

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 performs cross-format entity resolution and merges duplicates via reversible same_as edges. It distinguishes itself from siblings by specifying its use case and context.

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 guidance: 'Use once after a batch of june_remember/june_ingest writes, not per question; reads are already resolution-aware.' Also explains parameter toggling between conservative and fuzzy matching modes.

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