Community OSINT Trends

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

With strong annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false), the safety profile is fully covered by structured data. The description adds meaningful behavioral context beyond that: the keying on community tags rather than malware family, the data source provenance (tweetfeed.live, CC0), and the population caveat (<1% of upstream rows). This gives the agent a realistic expectation of data sparsity that annotations can't 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 compact given the breadth of data dimensions it names, and the critical distinguishing detail (corpus join, tag keying) is front-loaded near the start. The parenthetical sourcing note is efficient. It could save a few words on enumerating every data dimension, but it's an appropriately dense single block.

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 is a read-only aggregate view with an output schema present, so the description doesn't need to enumerate return fields. The description explains the data's source, its unique value proposition, and important caveats about tag population. For a no-parameter read tool, this level of contextual depth is adequate and complete.

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 tool has zero parameters and 100% schema coverage (empty schema), so there are no parameter semantics to document. The description instead provides rich context about the returned data dimensions (corpus coverage, early warning lead-time distribution), which substitutes for the parameter guidance that would normally be needed. With 0 params, the baseline is 4 per rubric.

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 identifies the tool as a community-driven trends view ('what the security community is surging on'), with specific data dimensions named (trending tags, TLD distribution, novelty, top producers, daily volume). The verb 'get' plus the resource 'trends' is specific, and the niche framing ('joined against our own corpus coverage,' 'exists nowhere else') helps differentiate it from siblings like get_platform_stats or get_landscape_briefing. However, the distinction from platform/landscape stats siblings is implicit rather than explicitly contrasted.

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 gives strong contextual guidance on what makes this tool unique ('the corpus join exists nowhere else') and clarifies important semantics ('Keyed on community TAGS, not malware family'). It tells the agent this covers a specific niche that other tools don't. However, it doesn't explicitly say when NOT to use it or name alternative tools for users wanting coverage-gap or lead-time reads in isolation.

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