get_topics

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

With no annotations, the description carries full burden and excels by disclosing that scope filtering parameters are silently ignored and that topic detection always runs across the full tenant graph. This is critical behavioral information beyond what the schema provides.

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 purpose and key behavior, with no extraneous content. Every sentence earns its place.

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 no output schema, the description mentions return type (labeled clusters with representative nodes and tags), which is adequate for an agent to infer the result structure. However, it could briefly describe the exact output format (e.g., array or object) for added clarity.

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?

Although all three parameters have schema descriptions indicating they partition memory, the description adds essential meaning by stating they are silently ignored and do not affect the operation. This is a significant correction to the agent's understanding.

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 detects topic clusters using community detection, returns labeled clusters with representative nodes and tags, and explains its purpose: to understand main themes in memory. This is specific and distinguishes it from siblings like query_graph or get_related.

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 says 'Use to understand the main themes in memory' and notes that scope filtering parameters are silently ignored, providing clear guidance on when to use and an important behavioral caveat. It does not explicitly mention when not to use or alternatives, but the context is clear.

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