protein_cost

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

With no annotations, the description carries the full burden, and it does disclose genuine behavioral facts the schema does not: the sort order (cheapest first) and the normalization unit (INR per 10 g protein). It says nothing about what happens when budget_inr is omitted (full ranking returned?), data freshness, or whether supplements and whole foods are interleaved in one list.

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?

One sentence, no waste, with the ranking and unit front-loaded. Nothing to trim and nothing padded.

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?

For a one-optional-parameter read tool with no output schema, the description covers what the result means (normalized cost, sorted) well enough to call it correctly. The only real gap is the default behavior when the optional budget filter is absent.

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 single optional parameter is fully documented in the schema (100% coverage), so baseline 3 applies. The description does not mention the budget filter at all, adding no meaning beyond the schema text.

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?

States a concrete resource (protein cost in India), unit (rupees per 10 g protein), scope (whole foods and supplements) and ordering (cheapest first). It is implicitly distinguishable from compare_foods and lookup_food, but never names them, so differentiation relies on inference.

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 'ranked cheapest first' framing implies a browse/rank use case, so an agent can guess this is the breadth-first cost view rather than a single-item lookup. However, there is no explicit when-to-use, no when-not-to-use, and no mention of the sibling tools that cover overlap.

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