codex_commodity_searchFind food commodities in the Codex pesticide residue database by name (apple, rice, milk, poultry meat). Returns each match with the id needed to look up every pesticide limit that applies to it. Codex splits foods finely — "apple" and "apple pomace, dried" are separate commodities with different limits.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Food or commodity name, e.g. "apple". |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only and idempotent. The description adds meaningful behavior: returns one or more matching commodities, exposes the required ID, and warns that Codex distinguishes fine-grained food forms with different limits. No contradiction with annotations.
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, then the output's purpose, then a useful caveat. No filler or duplication of the schema.
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-parameter search tool with full schema coverage and safety annotations, this description explains input domain, output content, and the next-step use of the returned ID. The fine-grained commodity caveat addresses the main ambiguity an agent would hit.
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 100% and includes a decent parameter description. The description adds value by providing varied examples (apple, rice, milk, poultry meat) and explaining granularity, e.g. 'apple' vs 'apple pomace, dried', which materially affects query expectations.
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 states a specific action ('Find food commodities') and a resource ('Codex pesticide residue database'), with named examples. It implies the commodity-search role rather than MRL lookup, but it never explicitly names or rules out sibling tools like codex_pesticide_search.
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 implies when to use this tool: before looking up pesticide limits, you need the commodity ID it returns. It does not explicitly state exclusions or point to an alternative tool for pesticide-name or MRL queries, leaving usage context implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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