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

With no annotations, the description bears full burden. It reveals paid pricing, live data sources (Open Food Facts, FDA recalls), and deterministic protein targets. However, it does not disclose authentication, rate limits, read-only nature, or failure modes, so behavioral transparency is only partial.

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 long but well-structured: a high-level summary, coverage line, and bullet list of endpoints with one-line purposes and pricing. It front-loads differentiators (GLP-1, UPF checks, supplement verification). Slightly verbose due to endpoint list redundancy with the schema enum, but each sentence adds value.

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?

This is a complex tool with 30 parameters and 15 endpoints, but the description lacks endpoint-parameter mapping, input format expectations, or return value details. Without an output schema and with only minimal per-endpoint guidance, an agent cannot reliably invoke the correct endpoint with correct parameters.

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%, but many descriptions are tautological ('Age', 'Sex', 'goal'). The description adds indirect context for some parameters (e.g., barcode for upf-check, brand for supplement-check) but fails to map parameters to specific endpoints, which is a critical gap for a multi-endpoint tool.

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 'Global nutrition intelligence API' and lists 15 distinct endpoints with specific verbs and resources (e.g., 'research: Nutrition research synthesis', 'upf-check: Ultra-processed food check by barcode'). This strongly distinguishes nutripulse from sibling pulse tools by focusing exclusively on nutrition.

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 endpoint list provides clear context for when to use each sub-action (e.g., 'glp1' for GLP-1 medication users, 'supplement-check' for brand verification). However, it does not explicitly mention alternatives (e.g., mealpulse, herbapulse) or state when not to use this tool, lacking explicit exclusions.

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