suggest_queries

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

No annotations are provided, so the description must disclose behavior. It states that suggestions are based on indexed content, but does not mention that the tool is read-only, or provide details on performance, licensing, or prerequisites beyond the implicit need for indexed 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 two clear sentences with no unnecessary words. It front-loads the core purpose and then provides additional context, making it efficient.

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?

The description covers the basic use case and hints at the suggestion types, but lacks details about the return format (expected output is not described) and any limitations or error conditions. For a simple autocomplete tool, it is adequate but not thorough.

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 the baseline is 3. The description adds context that the 'partial' parameter is a query string and mentions suggestion categories, but does not detail the 'max_suggestions' or 'category' parameters 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 uses a specific verb-resource pair ('Get autocomplete suggestions for partial queries') and explicitly lists the types of suggestions (technical terms, memory addresses, instructions, concepts), clearly differentiating from sibling tools like 'suggest_tags' which handle tags.

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 for autocomplete and mentions it's good for discovering searchable content and learning terminology, but does not explicitly state when to use it vs. alternative search tools (e.g., semantic_search, fuzzy_search) or when not to use it.

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