zuckerbot_research_competitors

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

No annotations provided, so description carries full burden. It discloses external data scraping (Meta Ad Library, web search) and return types. Could mention potential rate limits or data freshness, but overall transparent for a research 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: first explains action and outputs, second gives usage context. No redundant words, front-loaded with key information. Highly efficient.

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?

All parameters are documented in schema; description adds usage context and return types (positioning, hooks, gaps). Lacking explicit output format or example, but sufficient for a research tool with no output 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% with descriptions for all three parameters. Description adds minimal extra meaning—reiterates industry, location, and optional country refinement. Baseline score of 3 is appropriate given high schema coverage.

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 states the tool scrapes Meta Ad Library and web to analyze competitor ads, returns positioning, hooks, and gaps. It distinguishes itself from siblings like zuckerbot_research_market (broader) and zuckerbot_suggest_angles (different output).

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?

Explicitly says 'Use before creating a campaign to benchmark...' providing clear context. Does not explicitly state when not to use, but the sibling list offers alternatives. Slight deduction for missing exclusion criteria.

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