memory_recall

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

With no annotations, the description must fully disclose behavioral traits. It states the tool uses a precomputed vector but fails to mention any side effects, permissions, or error conditions (e.g., mismatched vector dimensions, missing memory store). This is insufficient for a tool with 9 parameters.

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 single sentence is concise but under-specified. It conveys the core action but lacks structure for quick scanning (e.g., bullet points or key highlights). It fits the tool's simplicity but could be more informative without being verbose.

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?

Despite 9 parameters and no output schema, the description does not explain return format, pagination, or error handling. For a similarity search tool that requires careful vector handling, this completeness gap is significant. The description should cover what the response contains and any limitations.

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%, meaning the input schema already describes each parameter. The tool description adds no additional meaning beyond the schema (e.g., it doesn't elaborate on the query vector or filtering behavior). Therefore, the contribution is minimal, scoring a baseline 3.

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 the resource 'memories from a store', and specifies the method 'by a precomputed query vector'. This distinctly separates it from sibling tools like memory_get (exact ID) or memory_list (list all), providing clear purpose differentiation.

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 provides no guidance on when to use this tool versus alternatives, such as memory_get for exact lookups or memory_list for browsing. It does not mention any prerequisites (e.g., embedding model) or exclusions, leaving the agent to infer usage context.

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