merkl_list_campaigns

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

No annotations provided, so description carries full burden. It thoroughly discloses data completeness (fully paginated, cached 15min memory + 30min disk), campaign types, and server-side filtering. This exceeds minimal expectations.

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?

Description is well-structured with sections, but somewhat lengthy (multiple paragraphs). It could be more concise, but every sentence adds value. No redundancy.

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?

Given no output schema and 4 parameters all described, the description is very complete: covers return data, caching, pagination, filtering, and campaign types. It fully equips the agent to use the tool correctly.

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 baseline is 3. Description adds value by explaining expected outputs and interpretation but does not add significant parameter details beyond the schema. It reiterates asset_filter behavior already in schema.

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 tool discovers live Merkl incentive campaigns, lists returned data (APR, reward tokens, daily rewards, TVL, campaign type), and provides examples. It distinguishes from sibling tools by focusing on Merkl-specific incentives, while siblings target other protocols like Morpho, Pendle, and Spectra.

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?

Description provides explicit use cases: verifying subsidized rates, finding opportunities, understanding yield stack. It suggests checking this tool when no subsidy is reported. However, it lacks explicit when-not-to-use guidance or alternative tools, but context is clear.

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