kb_journal

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

Annotations only indicate that this is a non-read-only, non-destructive write, so the description carries behavioral weight. It discloses what is created (atom with domain/tags/source), the return shape, and a required schema-mapping prerequisite; no contradiction with annotations.

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 dense sentences deliver behavior, comparison, return value, and prerequisite without wasted words. The core action is front-loaded.

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 small append operation with no output schema, this is nearly complete: it covers effect, return shape, and a critical precondition. The only notable omission is app_id semantics.

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%, and the description maps three of four params: content (free-text entry), tags, and source. The required app_id is never explained, which is a meaningful gap for a 0-coverage schema.

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 names a specific action ('Append a free-text journal entry'), a precise target (a new atom in domain 'journal'), and contrasts itself with knowledge_ingest. This makes the tool's identity unmistakable even among many sibling tools.

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?

It gives a clear use context for simple journal entries and explicitly contrasts with knowledge_ingest on domain choice and sensitivity tiering. It stops short of stating a direct 'use X when...' rule, but the alternative is identifiable.

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