discover_related_conversations

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

Annotations already indicate readOnly, destructive, idempotent, and openWorld hints. The description adds valuable behavioral context: it explains that the tool uses semantic clustering, groups results by topic, and includes platform indicators. This goes beyond annotations and helps the agent understand the tool's behavior.

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 well-structured with a title, bullet points, examples, and a response format section. It is front-loaded with key information. However, there is slight redundancy (e.g., repeating 'automatically discover' and 'Searches for memories'), making it slightly less concise than optimal.

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 (three parameters, no output schema), the description covers the main aspects: behavior, cross-platform nature, grouping, and platforms. Examples provide concrete scenarios. The response format is described at a high level, which is sufficient without an output schema. No critical gaps.

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 each parameter already has a description. The tool description adds minimal additional context for parameters, mainly implied through examples. It does not enhance understanding of parameter constraints or usage beyond what the schema 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: 'Find related conversations across ALL AI platforms' using semantic clustering. It specifies the verb (discover/find) and the resource (related conversations), and distinguishes from siblings like recall_memories by emphasizing cross-platform discovery and semantic grouping.

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 explicit guidance on when to use the tool (to discover related conversations across platforms) and includes practical examples. However, it does not explicitly mention when not to use it or suggest alternative tools among siblings, which slightly limits its completeness.

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