why_connected

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

With no annotations, the description fully discloses error behavior (loud failure for missing id or unmatched label), independent resolution of each side, mutual exclusivity of id and label parameters, and that only live entries are returned. It also includes an agent behavior instruction for presenting results.

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 dense and informative with no wasted words. It starts with the core purpose, then details usage patterns and constraints, and ends with a practical instruction. Each sentence earns its 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?

For a tool with 5 parameters and no output schema, the description covers error handling, parameter combinations, and references to the audit tool. However, it omits details about the return format (e.g., what a 'direct connection' looks like), which would enhance completeness.

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?

Although the input schema covers all parameters (100% coverage), the description adds significant meaning: distinguishing exact vs fuzzy lookup, error conditions, and mixing rules. It goes beyond the schema's descriptions to clarify usage patterns.

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 'Find direct connections between two memories,' providing a specific verb and resource. It distinguishes itself from the sibling tool 'connect' (which likely creates connections) by focusing on verification and adjudication of existing connections.

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 explicitly advises when to use from_id/to_id for exact pair verification vs from_label/to_label for fuzzy lookup. It also directs users to audit(mode=archived) for archived memories and audit(mode=stale) for drift candidates, providing clear alternatives.

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