Get AI-powered answers with citations and reasoning. Use when you need synthesized answers rather than raw search results. Providers: kagi_fastgpt (fast answers), exa_answer (semantic AI), linkup (deep agentic search), tavily_research (asynchronous multi-search reports; resubmit its research_id to retrieve results).

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

Annotations already cover read-only/idempotent safety, so the description adds value by disclosing asynchronous retrieval behavior for tavily_research ('resubmit its research_id'), provider-specific behaviors, and the output nature (citations and reasoning). No contradiction with annotations.

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?

Two sentences with the purpose front-loaded and provider details compactly listed. Every clause contributes meaning without unnecessary elaboration.

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?

Covers when-to-use, provider differences, and the async resubmission pattern, and annotations cover safety. However, it omits response structure and contains a provider/enum inconsistency, leaving an agent with an ambiguous picture for a 5-parameter tool without an output schema.

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 description need not repeat parameter details, but it adds provider characteristics that conflict with the enum by naming exa_answer and linkup which are not valid values. It does usefully explain research_id for Tavily, but the misinformation undermines reliability.

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?

Tautological: description restates name/title.

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?

Explicitly says 'Use when you need synthesized answers rather than raw search results', giving a clear when-to-use signal. However, it lists exa_answer and linkup as providers even though the schema enum only allows kagi_fastgpt and tavily_research, making the provider-selection guidance partially misleading.

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