Evaluate typed choice, score, and noul questions using configured models, with optional images and state, to return structured answers for MCP clients.
Answer typed choice/score/noul questions with a System One model (jev/nimble/kev/tev/laya).
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| state | Yes | ||
| images | No | ||
| questions | Yes | ||
| keep_alive | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It says nothing about whether models must be loaded, what state represents, what keep_alive does, latency characteristics, or what the response contains. For a tool with nested question objects and no output schema, this is a significant gap.
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 efficient sentence with no wasted words, and the core verb is front-loaded. It is terse to the point of under-specification, but structurally sound.
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?
Given a complex nested schema (three question-type variants), 5 undocumented parameters, no annotations, and no output schema, the description is far too thin. An agent cannot determine valid state/image formats or how question criteria should be shaped.
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 5 parameters. The description maps loosely onto two of them (model names and the 'choice/score/noul' question types), but state, images, and keep_alive are entirely undocumented in both schema and description. It does not compensate for the coverage gap.
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+resource: 'Answer typed choice/score/noul questions with a System One model.' It also enumerates the supported models (jev/nimble/kev/tev/laya), which is concrete. However, it does not distinguish itself from the sibling systemone_list_models or clarify what 'noul' means, so it stops short of 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?
There is no guidance on when to use this tool versus alternatives, and the sibling systemone_list_models (presumably for discovering valid model names) is never mentioned. The model list is embedded but without stating that a call to list_models may be a prerequisite.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.