backfill_embeddings

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

With no annotations provided, the description carries the full burden, and it does a good job: it explains parameter effects (force, dry_run, limit), lists possible exceptions (ValueError, ImportError), and summarizes the return value. It could go further by noting potential resource cost or persistence side effects, but the core behavior is clear.

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 a one-sentence purpose followed by Args, Returns, and Raises sections. Every line earns its place and there is no fluff or redundancy with the schema.

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 four-parameter tool with no annotations, the description is sufficiently complete: it covers all parameters, return fields, and error conditions. An agent can understand what the tool does, how to invoke it, and what to expect in response without needing additional context.

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?

The schema has 0% description coverage, so the description fully compensates by explaining all four parameters: model with default, limit range, force to regenerate, and dry_run as preview-only. This adds meaning far beyond the bare schema types and defaults.

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 states a specific action ('Generate embeddings') and a clear resource/scope ('for memories that lack them'). This distinguishes it from sibling tools like search_memory or cluster_memories, which have different purposes.

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 usage context is implied by the title and first line: use when memories are missing embeddings. However, there is no explicit guidance about when not to use it or how it compares to alternatives among the sibling tools.

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