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

The description discloses the hybrid search behavior and support for exact terms (paper IDs, author names), which is useful. However, there are no annotations, so the description carries the full burden for behavioral transparency. It does not mention that the tool is read-only, any rate limits, or important coverage caveats like the vectorModel space limitation (which is only in the schema, not the description). Thus, it is adequate but has clear 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?

The description is three sentences long, front-loaded with the core purpose, and every sentence adds value. The first sentence explains what the tool does, the second enumerates filtering capabilities, and the third gives usage guidance with named alternatives. There is zero redundancy or filler.

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?

For a tool with 14 parameters and many sibling tools, the description provides enough context to decide when to use it: it states the hybrid nature, default status, and key filters. It does not explain the fast/rerank strategy or run_id attribution, but those are detailed in the schema. Given no output schema, the description is reasonably complete for decision-making, though not exhaustive.

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?

The input schema has 100% parameter description coverage, so the baseline is 3. The description mentions filtering by content type, entities, categories, and date range, which map to existing schema properties but add no new semantic meaning beyond what the schema already provides. Therefore, the description does not significantly enhance parameter understanding beyond the structured schema.

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 the tool's function with a specific verb and resource: 'Hybrid semantic + keyword search across scientific papers.' It explicitly mentions combining vector similarity with BM25 full-text matching, and distinguishes it from sibling tools by calling it the 'Default mode for general queries.' This clearly sets it apart from search_keyword and search_semantic.

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 explicitly says when to use this tool ('Default mode for general queries') and provides direct alternatives: 'use search_keyword for exact-term lookups or search_semantic for pure paraphrase queries.' It also lists supported filtering dimensions, giving the agent concrete guidance on when this tool is appropriate versus its siblings. This meets the highest bar for usage guidance.

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