recall

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the search mechanism (semantic similarity) but doesn't disclose whether this is a read-only operation, how results are ordered, whether it triggers side effects like memory consolidation, or any rate limits. For a search tool with no annotations, this is a significant gap.

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 concise sentence that front-loads the core purpose. It earns its place with no wasted words, though it could add a brief usage note without becoming bloated.

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?

With no output schema and no annotations, the description is incomplete for a tool with six parameters. It doesn't explain return format, ordering, or how semantic similarity is computed. The schema covers parameters, but the description doesn't provide enough context for an agent to know what to expect from the call or when to use it.

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 all six parameters. The description adds no additional parameter semantics beyond what the schema provides. Baseline 3 is appropriate since the schema does the heavy lifting.

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 'Search for similar memories using semantic similarity' clearly states the verb (search), resource (memories), and method (semantic similarity). It distinguishes itself from sibling tools like 'hybrid_recall' and 'nearby' by specifying semantic similarity, though it doesn't explicitly name alternatives.

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 finding similar memories but provides no explicit guidance on when to use this tool versus alternatives like 'hybrid_recall' or 'nearby'. The schema parameters (namespace, min_similarity, project) hint at filtering use cases, but the description itself doesn't state when to choose this over siblings.

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