reddit_trending

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true. The description adds that the tool uses keyword extraction (cashtags + uppercase words) and not NLP, allowing false positives. This disclosure of non-obvious behavior surpasses what annotations convey.

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 four sentences, all essential: states purpose, lists subreddits, describes return format, and notes limitations. No redundant or filler language. Efficiently front-loaded.

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?

With no output schema, the description explains that results include tickers sorted by mention count with a per-subreddit breakdown, satisfying understanding of return value. Complexity is low (two optional parameters), and the description covers usage, limitations, and output format completely.

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%, meaning both parameters ('subreddits' and 'limit') are already described in the schema. The description adds no new semantic information about parameters beyond what the schema provides. Baseline score of 3 is appropriate.

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 the tool retrieves trending stock tickers from Reddit based on mention frequency, specifying subreddits scanned (r/wallstreetbets, r/stocks, r/investing, r/options) and that returns sorted tickers with per-subreddit breakdown. This distinguishes it from siblings like reddit_sentiment which focuses on sentiment analysis.

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 mentions it's 'best for gauging retail buzz, not precise sentiment,' giving implicit guidance on when to use it. It also notes the limitation of false positives. However, it does not explicitly state when not to use it or name alternative tools for precise sentiment (e.g., reddit_sentiment).

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