add_observations

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

No annotations are provided, so the description carries the full burden. It discloses that observations should be atomic facts and warns against dumping paragraphs. However, it does not mention expectations about entity existence, overwrite behavior, or permissions, but the core behavioral traits are covered.

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 concise sentences that front-load the purpose and immediately follow with usage rules. No wasted words.

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?

Given no annotations, no output schema, and a simple nested array parameter, the description fully covers the tool's usage, format guidelines, and distinction from related tools. Nothing essential is missing.

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% according to signals, but the description compensates by explaining the format of contents: one fact per observation, use prefixes, avoid paragraphs. This adds significant meaning beyond the schema property names.

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 'Add atomic facts to existing entities' clearly states the verb (add) and resource (atomic facts to entities). It also distinguishes from sibling tool create_relations by specifying that relations should use that tool instead.

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: one fact per observation, avoid paragraphs, use prefixes like 'Gotcha:', 'Warning:', etc. Directs agents to use create_relations for relations, giving clear when-to-use and when-not-to-use instructions.

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