auto_link_observations

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

No annotations provided, so description carries full burden. It discloses that the tool detects entity mentions and suggests cross-reference relations. While it doesn't detail side effects or whether suggestions are automatic, the behavior is reasonably clear for a detection 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?

Two sentences with no redundancy. Front-loaded with action and key differentiator.

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?

Simple tool with one parameter and clear purpose. Description fully covers what the tool does and how it differs from a sibling, without needing output schema or additional details.

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 100% with description for 'text' parameter. Description adds context that the tool scans for entity mentions and suggests relations, going beyond the schema's simple description. Score above baseline 3 due to added value.

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?

Describes specific verb (detect, suggest) and resource (observation text, cross-reference relations). Explicitly distinguishes from sibling normalize_observations by stating it finds entity name mentions rather than resolving pronouns/dates.

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?

Explicitly states when to use this tool vs normalize_observations, providing a clear alternative for cross-referencing entity mentions.

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