search_and_summarize

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

With no annotations, the description takes full burden. It discloses use of local LLM qwen3:14b, fallback to raw content if Ollama unavailable, and structured answer with citations. However, it omits potential latency, prerequisite of Ollama running, or any permissions needed. This is adequate but leaves gaps.

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, each adding distinct value: process chain, fallback, best-use case. No redundant words. Front-loaded with key actions. Perfectly concise.

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 7 parameters, no output schema, and no annotations, the description explains the process and fallback but lacks details on output structure beyond 'structured answer with source attribution'. Missing info on rate limits, authentication, or resource requirements. 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% with each parameter described. The description does not elaborate further on parameter values or usage beyond the schema. Baseline 3 is appropriate as the description adds no additional parameter-level insight.

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 defines the tool as a multi-step process: search, rerank, fetch, synthesize summary with citations. It explicitly contrasts with raw page tools, distinguishing it from siblings like search and search_and_fetch. The verb 'synthesize' and resource 'summary' are specific.

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 phrase 'Best for deep research where you want pre-digested synthesis rather than raw pages' provides clear context on when to use it. It implicitly advises against using it when raw pages are preferred, though no explicit alternative names are given. The fallback behavior is mentioned, which helps the agent gauge reliability.

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