search_humor

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

There are no annotations to rely on, so the description carries the transparency burden. It discloses return content (lines with credit) and a heuristic behavior on whole_lines. However, it doesn't explain pagination behavior, how the sort/ordering works, or what happens with special inputs — moderate disclosure but with gaps for a read/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?

Two sentences that front-load the core purpose and immediately list the key filters. Zero waste — every clause carries meaning. The return description ('lines with their credit attached') is folded neatly into the first sentence.

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?

This is a moderately complex tool with 8 parameters but no output schema. The description covers the search-and-filter story well but doesn't explain the default ordering when query is empty, the interaction between source and the gating flags, or how results are ordered/ranked. For a search tool with no output schema, slightly more about result shape and ordering would improve completeness, though the parameters are well-covered by the 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 the schema documents all 8 parameters. The description adds semantic value by grouping parameters into meaningful categories (source pack, kind, minimum human score), which helps the agent reason about which filters apply together. It also labels whole_lines as 'heuristic' and 'strict about a final full stop,' adding behavioral nuance beyond the 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 clearly states it performs 'full-text search the humor corpus' and returns 'lines with their credit attached.' It also names the filter dimensions (source pack, kind, minimum human score), distinguishing it from siblings like top_rated, corpus_stats, and sources which serve different purposes.

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 implies search/filtering use cases via the filtering verbs, but doesn't explicitly state when to choose this over alternatives like top_rated, taste_profile, or breakdown. There's no when-not-to-use guidance, though the 'browse by score' hint in the query param schema partially indicates an alternative to top_rated.

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