reddit_leads

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

With no annotations provided, the description carries the full disclosure burden and does it well. It reveals the filtering logic, the always-running deterministic prefilter, the conditional LLM refinement path, and the 503 Retry-After throttling behavior. This goes well beyond a generic 'search Reddit' claim and helps an agent predict side effects and error handling.

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?

Every sentence earns its place. The description front-loads the core purpose, then layers filters, classifier behavior, and error handling in a logical order. It is long but dense with directly useful operational detail, with no filler or redundant restatement of the tool name.

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?

The tool has 7 parameters and no output schema, yet the description provides enough context to invoke it correctly: what inputs are expected, what filtering happens, how scoring works, and how to handle throttling. The return format is only partially specified ('ranked highest-first with the signals that fired'), so an agent might not know the exact lead fields, but this is sufficient for a first call.

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 valuable meaning beyond the schema by explaining that `q` should be a plain-language description of what you offer, and by clarifying the `classifier` parameter's runtime behavior (`llm` triggers an additional batched model refinement). Other parameters like `sort`, `time`, `limit`, and `min_score` are not elaborated further, but the schema already defines them clearly.

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 'Find Reddit buying-intent leads', a specific verb-plus-resource statement. It then details the mechanism (scanning Reddit search pages, scoring 0-10, ranking highest-first) and lists exact exclusion categories, making it clearly distinct from general Reddit search tools like reddit_search or reddit_comments.

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 clearly implies when to use it: when you want to find people actively asking for a product or service. However, it does not explicitly name alternatives or state when NOT to use it, such as when raw Reddit posts or comments are needed instead of scored leads. The usage context is present but exclusions are left to inference.

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