Astria-Index

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

With no annotations provided, the description carries the full burden and successfully discloses key behaviors: idempotent recording (confidence increments on duplicates), automatic surfacing of patterns, and return value structure (insight ID and observation count). It could further improve by mentioning persistence scope or deletion capabilities (given the 'forget' sibling exists).

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 well-structured with clear sections (concept, usage guidelines, arguments, returns). Every sentence adds value—conceptual framing establishes purpose, examples clarify boundaries, and argument descriptions enable correct invocation. No redundancy or filler text despite the length.

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 the tool's complexity (semantic triple storage with deduplication logic) and the presence of an output schema, the description is complete. It explains the return values meaningfully and differentiates the tool's role within the broader memory ecosystem of siblings without needing to duplicate schema specifications.

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%, requiring the description to fully compensate. The Args section provides rich semantic meaning for all four parameters, including the confidence scale's interpretation (0.5 for hypotheses, 0.7 for likely, 1.0 for confirmed) which is critical for correct usage and not inferable from the schema types alone.

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 opening sentence 'Record a cause-and-effect pattern you've discovered' uses a specific verb and resource. It clearly distinguishes from sibling tools like 'remember' or 'save_note' by framing insights as 'the deepest memory tier — understanding WHY things happen,' establishing a clear conceptual hierarchy.

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 'Use this when:' section provides three concrete, contextualized examples (recurring bugs, workflow patterns, root cause investigation) that explicitly bound when to invoke this tool versus simpler memory storage. The examples include realistic parameter values, making the activation conditions unambiguous.

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