search_faceted

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

With no annotations provided, the description carries the full behavioral burden. It transparently discloses FTS relevance ranking vs newest-first ordering, partial matching for disposition, and the 1997+ voting-data constraint affecting dissent/concurrence/unanimity filters. It does not discuss error handling, rate limits, or AND/OR combination semantics, but covers the key decision-relevant behaviors.

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 with no filler. The core purpose is front-loaded, followed by the most important behavioral caveats. Every sentence earns its place and the text remains easy to scan.

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 10 optional parameters, no annotations, full schema coverage, and an output schema, the description includes the crucial behavioral nuances: ranking behavior, default ordering, partial matching, and the voting-data date limitation. It is slightly incomplete because it does not explicitly state how multiple facets combine or provide routing guidance relative to similar search tools.

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 the baseline is 3. The description adds extra meaning beyond the schema by clarifying that query triggers FTS relevance ranking, that disposition is a partial match with a concrete example, and that voting-based filters only apply to 1997+ opinions. This enriches an otherwise schema-driven 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 opens with 'Faceted opinion search' and immediately specifies the resource (opinions), the operation (search), and the differentiator (filtering by metadata plus optional full text). This clearly distinguishes it from general search tools like search_opinions and search_boolean.

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 explains that any combination of facets may be supplied and describes the default ordering and relevance ranking, which implies when the tool is appropriate. However, it does not explicitly state when to prefer this tool over siblings such as search_opinions or search_boolean, nor does it mention when-not-to-use scenarios.

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