@git-fabric/aiana

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

With no annotations provided, the description carries full burden. It discloses the tool's behavior: using project name as a semantic seed and returning top-N most relevant memories. However, it lacks details on permissions, rate limits, or what constitutes 'relevant' (e.g., recency, frequency).

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 a single, well-structured sentence that efficiently conveys purpose, method, and output. Every word earns its place with no redundancy or fluff.

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 and no output schema, the description is adequate for a read-only recall tool but incomplete. It doesn't explain return format, error handling, or how 'relevance' is determined, which could be important for an AI agent to use it effectively.

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 100%, so the schema already documents both parameters. The description adds marginal value by mentioning 'project name as a semantic seed' and 'top-N most relevant memories', which aligns with but doesn't significantly expand beyond the schema's parameter descriptions.

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 the verb ('recall') and resource ('memories') with specific scope ('for a project'). It distinguishes from siblings like aiana_memory_add (add), aiana_memory_delete (delete), and aiana_memory_search (search) by focusing on semantic recall of top-N relevant memories.

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 implies usage context ('for a project') and mentions scoping to that project, but does not explicitly state when to use this tool versus alternatives like aiana_memory_search. No exclusions or prerequisites are provided, leaving some ambiguity.

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