smart_suggest

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool analyzes patterns and provides personalized suggestions, but doesn't disclose important behavioral traits like whether this requires authentication, how it accesses past searches/memories, potential rate limits, privacy implications, or what happens when no suggestions are available. The description is insufficient for a tool that presumably accesses user data.

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 perfectly concise with two focused sentences that each earn their place. The first sentence states the core functionality, and the second explains the value proposition. There's zero wasted language or 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 the tool's moderate complexity (personalized recommendations based on user data), no annotations, and an output schema that presumably documents return values, the description is incomplete. It doesn't address authentication needs, data access patterns, error conditions, or the nature of the suggestions. The output schema reduces the need to describe return values, but other behavioral aspects remain undocumented.

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?

The input schema has 0% description coverage, providing only a parameter name and type. The description doesn't mention any parameters at all, so it adds no semantic information beyond what the bare schema provides. With only one optional parameter and an output schema present, this is minimally adequate but leaves the 'max_suggestions' parameter completely unexplained in natural language.

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: 'Get personalized query suggestions based on your past searches and memories' with the specific verb 'Get' and resource 'personalized query suggestions'. It distinguishes itself from siblings like 'search_documents' or 'unified_search' by focusing on personalized recommendations rather than direct searching. However, it doesn't explicitly contrast with similar tools like 'suggest_cleanup' or 'explore_connections'.

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 implies usage context ('based on your past searches and memories') and suggests when to use it ('recommend what you might want to explore next'), but doesn't provide explicit guidance on when to choose this tool over alternatives like 'suggest_cleanup' or 'explore_connections'. No exclusions or prerequisites are mentioned.

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