Lynceus web search

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

With no annotations provided, the description carries the full burden. It discloses returns (numbered list with rank, title, URL, snippet, published date), snippet brevity and the need to call lyn_extract for full text, cost (1 credit per request, cached results still return data), and failure modes with handling (engine_upstream retry once, 401 bad key, 402 out of credits). This is comprehensive for a read-only search tool.

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 organized into logical sections (WHEN TO USE, ARGUMENTS, COST, RETURNS, FAILURES) with front-loaded purpose and usage. Every sentence carries information, and it avoids redundancy with the schema. Despite being longer than average, the structure makes it efficient to parse.

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 complexity (3 parameters, no output schema, no annotations), the description covers all essential aspects: purpose, usage context, parameter semantics, return format, errors, cost, and sibling relationship. Nothing an agent needs to call it correctly is missing. The output format is described even 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 baseline is 3. The description adds meaningful guidance beyond the schema: query suggests 1–10 words, natural language is fine, and locale alignment; freshness explains enum meanings with defaults; max_results gives a default. It also explains expected behavior with omitted optional parameters. This elevates it above baseline.

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 states a specific verb ('Search the live web via Lynceus'), a resource (Lynceus with Yandex index), and explicitly distinguishes from the sibling lyn_extract ('Use BEFORE lyn_extract when you don't yet have the URLs'). It also notes its RU-web-first strength, clarifying its niche versus Google-based tools. This fully differentiates it.

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?

There is a dedicated 'WHEN TO USE' section that lists concrete scenarios (news, docs, prices, people, Russian sites) and explicitly names the alternative tool and the condition that selects it ('Use BEFORE lyn_extract when you don't yet have the URLs'). It also mentions when NOT to use it implicitly by pointing to lyn_extract for full-text extraction.

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