search_and_summarize

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

With no annotations, the description carries full burden. It discloses the use of a local LLM (qwen3:14b), fallback to raw content if Ollama is unavailable, and a structured answer with citations. Omits details like rate limits or external request behavior, but covers key aspects.

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?

Three sentences that front-load the core action and fallback, then mention return format and usage guidance. No wasted words; each sentence adds value.

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 9 parameters with 100% coverage and no output schema, the description explains the workflow (search, rerank, fetch, synthesize), fallback, and output structure. Lacks detail on 'rerank' and exact output schema, but provides enough for competent use.

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 baseline 3. The description adds useful context beyond schema: site restriction is 'best-effort', engines 'forwarded verbatim with graceful degradation', fetch_count defaults and max, etc. Enhances parameter 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 explicitly states the tool's purpose: search, rerank, fetch top results, and synthesize a summary using a local LLM. It distinguishes from siblings like 'search' (raw results) and 'search_and_fetch' (fetch without synthesis) by emphasizing the synthesis and citation component.

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 clearly advises use for 'deep research where you want pre-digested synthesis rather than raw pages,' implying a contrast with sibling tools. It does not explicitly list when not to use, but the guidance is sufficient for appropriate selection.

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