list_feedingsRetrieve farm feeding records, including executed mixer loads, newest first, with ration, status, batch scale factor, and total pounds delivered.
List feedings (executed mixer loads) on the farm, newest first. Each shows the ration, status, batch scale factor, and total pounds delivered.
| Name | Required | Description | Default |
|---|---|---|---|
| skip | No | ||
| take | No | ||
| since | No | ||
| status | No | ||
| farm_id | No |
Changes observed during successful MCP inspections.
v0.2.0Input schema / $schemaAdded value: +"http://json-schema.org/draft-07/schema#"Input schema / additionalPropertiesAdded value: +falseInput schema / properties / farm_id / descriptionRemoved value: -"The ID of the farm (optional if a default is set)."Input schema / properties / since / descriptionRemoved value: -"ISO datetime; only feedings fed at or after this time."Input schema / properties / skip / descriptionRemoved value: -"Records to skip (pagination)."Input schema / properties / status / descriptionRemoved value: -"Filter by feeding status (ACTIVE = still in progress / resumable)."Input schema / properties / take / descriptionRemoved value: -"Max records to return."v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds genuinely useful behavior beyond that: newest-first ordering and the fact that each row carries ration, status, batch scale factor, and total pounds delivered. It says nothing about pagination behavior or result-set size, which matters for a list endpoint.
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?
Two compact sentences, front-loaded with the action and scope, then the returned content. Every clause carries information and nothing is 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?
With no output schema, the sentence describing returned fields (ration, status, batch scale factor, total pounds) usefully covers the response shape. However, the five undocumented filter/pagination parameters and unstated result limits leave real gaps for an agent trying to invoke it correctly.
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 0% across five parameters (skip, take, since, status, farm_id), and the description explains none of them - not what 'since' means, not the ACTIVE/COMPLETED status values, not the take maximum. The mention of 'status' refers to a returned field, not the filter argument, so it does not compensate.
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 specific verb and resource ('List feedings') and immediately disambiguates the domain term by glossing it as 'executed mixer loads', plus adds the ordering ('newest first'). It does not explicitly separate itself from the nearby get_feeding/list_rations siblings, so it falls short of the 5-mark sibling differentiation bar.
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?
There is no when-to-use guidance, no mention of when to prefer get_feeding over this list call, and no reference to the available filters (farm_id, status, since) or pagination. Usage must be inferred from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.