read_label

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

With no annotations, the description carries the full load and does so well: it discloses local processing, the reader default chain ($FOODDB__BACKEND__LABEL_READER then claude-cli), the cost implication of subscription usage, and the exact data-egress path (SUBMIT_URL + SUBMIT_KEY) when submit is true. It omits whether a read persists to the local DB and what happens on reader failure.

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?

Three sentences, front-loaded with the core action and output before the environment/egress details. The env-var references add density but each sentence carries real behavioral information, so little is wasted.

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?

An output schema exists, so return values need no explanation, and the description covers the important non-obvious behavior: local-first processing and the conditional submission flow. It is incomplete only on the undocumented name/barcode params and persistence side effects.

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 0%, so the description must compensate and only partly does: it documents image format (base64 JPEG/PNG/WebP), reader defaults, and submit semantics. The name and barcode parameters are never mentioned anywhere, leaving 2 of 5 parameters semantically opaque.

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?

Names a specific verb and resource (read a nutrition label photo) plus the output shape (per-100 values) and an execution constraint (on this machine). An agent can distinguish this from get_product_by_barcode or get_food. It stops short of naming any sibling explicitly, which is the only thing keeping it from a 5.

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?

Implies when to use it (you have a label photo to convert) and describes the submit condition that changes tool behavior, but never contrasts it against siblings like get_product_by_barcode or says when NOT to use it. Usage is inferable rather than stated.

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