query

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

With no annotations, the description must cover behavioral traits. It explains the use of semantic similarity and combination of vector search with knowledge graph facts, but lacks details on side effects (none assumed), output format, or any destructive implications. This is adequate but not exhaustive.

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 two sentences, front-loaded with the key instruction 'Retrieve context BEFORE answering'. Every sentence adds value, and there is no redundancy.

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 10 parameters, no output schema, and no annotations, the description is somewhat incomplete. It explains the core behavior but does not elaborate on parameter interactions (e.g., default volume_id, effect of rerank) or output details. A more comprehensive description would benefit the agent.

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%, so baseline is 3. The description adds high-level context about how the tool processes queries (semantic similarity, vector + knowledge graph), but does not clarify individual parameter meanings beyond what the schema already provides.

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's purpose: retrieving context before answering by searching SharedMemory via semantic similarity. It distinguishes from siblings like get_memory (specific retrieval) and search_entities (entity search).

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 explicitly advises using this tool 'BEFORE answering', providing clear context. However, it does not mention when not to use it or suggest alternatives like get_memory for direct lookups.

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