The ready-made workloads of cheapest_for_workload: name, what job it is, tokens a month, context needed, model type.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "list_workload_presets"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so safety is covered; the description adds genuine value by disclosing the shape of each returned record (name, job, monthly tokens, context requirement, model type). With no output schema present, this return-field disclosure is the main behavioral payload, though it doesn't say how many presets exist or how to consume them downstream.
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?
A single compact sentence that front-loads the resource and then lists return fields — no filler or redundancy. The phrasing 'the ready-made workloads of cheapest_for_workload' is slightly elliptical and would read better as an explicit list verb.
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 zero-parameter read-only listing tool with no output schema, the description covers what an agent needs: what the list is, its relationship to the consuming tool, and the fields per entry. Only the entry count and whether the presets are literal accepted inputs remain unspecified.
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 tool takes zero parameters, so there is nothing to disambiguate and the description correctly doesn't invent parameter discussion. Baseline 4 applies for a parameterless 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 identifies the resource (ready-made workload presets) and enumerates what each entry contains, and it ties itself to the sibling cheapest_for_workload so an agent can tell the two apart. It lacks an explicit verb like 'list', leaving the action implicit in the name, but the intent is unambiguous.
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?
By calling these the 'ready-made workloads of cheapest_for_workload', it implies this is the lookup step before calling that sibling, which is useful usage context. However, it never states an explicit when-to-use rule, never excludes alternatives like cheapest_models_by_task, and doesn't say whether results should be passed verbatim as inputs.
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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