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

With no annotations, the description carries the full burden. It adds transparency by disclosing per-endpoint pricing (e.g., '$0.15'), data sources, and geographical coverage, which are useful behavioral details. It does not mention rate limits, authentication, response format, or side effects, but as a read-only intelligence API, the key risks are adequately covered.

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 well-structured: it front-loads the core purpose, then uses a bulleted endpoint list with prices. It is informative but not overly verbose. The pricing information is extra but arguably useful for selection, though it occupies space that could otherwise detail endpoint input requirements.

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?

Without an output schema or annotations, the description must explain what each endpoint returns and any endpoint-specific constraints. It gives high-level summaries but does not map required parameters (e.g., which endpoints require lat/lon vs. region, or crop) or describe the response structure. For a tool with 10 endpoints and 8 parameters, this leaves invocation details under-specified.

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 input schema covers 100% of parameters with descriptions, so the baseline is 3. The description adds value beyond the schema by providing concise, action-specific semantics in the endpoint list, such as 'weather-risk: 7-day crop-specific weather risk assessment', which clarifies what each 'action' enum value returns without needing to refer to separate documentation.

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 'Global precision agriculture intelligence API' and enumerates 10 specific endpoints (e.g., yield-forecast, weather-risk, soil-intel), establishing a concrete verb-resource relationship. It distinguishes itself from sibling tools by focusing on agriculture and naming unique data sources (NDVI, USDA WASDE, FAO).

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 provides clear functional context by listing coverage ('Global'), target users ('growers, agronomists'), and endpoint domains. However, it does not explicitly state when to prefer this tool over sibling pulse tools or mention exclusions/alternatives, leaving some ambiguity for related topics like climate or water.

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