Lists all pypto-lib workloads or one model's kernels split into prefill and decode.
List pypto-lib model workloads, or one model's kernels split into prefill and decode.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Optional model directory name, e.g. "deepseek_v4_1_flash". Empty lists every pypto-lib workload. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.3.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that a named model returns kernels organized into prefill and decode phases, which is non-obvious, but says nothing about permissions, result size, or pagination for what is presumably a potentially large listing.
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 verb and resource, with the conditional behavior folded in cleanly and no wasted words.
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 only one optional parameter, full schema coverage, and an existing output schema to explain return values, the description is nearly complete. The main minor gap is the absence of any usage context relative to sibling tools.
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%, so the 'name' parameter's format and empty-string default are already documented. The description adds value by explaining what the parameter's effect produces structurally (per-model kernels split into prefill/decode), going beyond the schema's simple 'lists every workload' phrasing.
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 opens with a specific verb+resource ('List pypto-lib model workloads') and clarifies the dual mode of operation (all workloads vs. one model's kernels split into prefill/decode). It is unambiguous, though there is no sibling with overlapping purpose to explicitly differentiate from.
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?
Usage is only implied: passing a name yields one model's kernels, leaving it empty lists everything. There is no explicit when-to-use/when-not guidance, no mention of prerequisites, and no named alternative among the many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.