Community Campaigns

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral details beyond annotations: upstream proxy failure returns community_error instead of erroring, and cluster labels are upstream AI output rather than Threadlinqs attribution. No contradiction with annotations.

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?

Three sentences with no redundant content. Each sentence earns its place: first lists the contents, second clarifies data provenance, third explains error handling. Very concise and 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 an output schema present, return values are documented. The description covers purpose, data source, content fields, provenance caveat, and error behavior. Only missing explicit alternative tool comparisons, which for a simple read-only list tool is an acceptable gap.

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 has 100% coverage for the single parameter 'limit' with its description (default 15, max 50). The tool description adds no further information about parameter usage, so baseline 3 applies.

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?

Description clearly identifies what is returned (campaign clusters from community OSINT layer) and lists specific fields (cluster name, confidence, targeted brand, first/last seen, indicator count/types, tags, reporters). It distinguishes from siblings by naming the source (tweetfeed.live, CC0) and explicitly stating that cluster labels are upstream AI output, not Threadlinqs attribution.

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?

Implied usage context is strong: the tool provides community-layer campaign data, and the description gives guidance on how to interpret labels (do not present as our assessment) and how to handle errors (report 'community feed unavailable' rather than 'no campaigns'). However, no explicit alternative tools or when-not-to-use scenarios are named.

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