setup_ai_memory

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

With no annotations, the description carries full burden. It discloses key behaviors: returns content, optionally writes to project directory, and uses embed provider. However, it omits error handling, prerequisites (e.g., instance validity), and side effects beyond file writing.

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 layers and front-loads the main purpose. It is slightly lengthy but every sentence adds value without fluff. Could be tightened for brevity.

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 (3-layer system), schema coverage (100%), and no output schema, the description covers purpose, return value, and optional behavior. Lacks details on error handling and prerequisites, but is reasonably complete for a setup tool.

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 is 100%, providing baseline of 3. The description adds useful context like default embed provider and local setup hint, and clarifies optional writing behavior. This adds value beyond the schema definitions.

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 identifies the tool as a one-shot setup of a 3-layer AI memory system, listing each layer's components and its outcome. It distinguishes itself from sibling tools that perform individual memory operations (e.g., learn_from_attempts, smart_recall).

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 for initial project setup but provides no explicit guidance on when to use this tool versus alternatives like individual memory tools. No 'when not to use' or specific contexts are mentioned.

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