recall

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions semantic search using Jina embeddings and vector similarity but fails to detail important behavioral traits such as authentication requirements, rate limits, or behavior when no matches are found. This lack of depth limits transparency.

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 concise with four sentences, each serving a purpose: stating the function, expanding on scope, giving usage guidance, and emphasizing primacy. No redundant or unnecessary information is present.

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 (12 parameters, no output schema), the description provides a sufficient high-level context for an agent to understand when and why to use it. The schema handles parameter details, so the description is adequately complete for its role.

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 parameters are already well-documented. The description adds high-level context but does not enhance understanding of individual parameters beyond what the schema provides, resulting in minimal added value.

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 tool performs semantic recall from long-term memory using vector similarity, specifying it searches across promoted insights, digests, and archived knowledge. It positions itself as the primary tool for institutional memory, but does not explicitly differentiate from sibling tool 'search_memories', which may serve a similar function.

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 instructs to use this tool for retrieving established patterns, past decisions, documented workflows, or any knowledge promoted to long-term storage, providing clear context. However, it does not specify when not to use it or mention alternative tools like search_memories, leaving some ambiguity.

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