Evaluate a named map of Noul, Choice, and Score questions to obtain probabilistic AI decisions for classification, scoring, and validation.
Evaluate a named map of Noul, Choice, and Score questions.
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | ||
| questions | Yes | ||
| logging_exclusions | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 of behavioral disclosure. It does not mention side effects, state handling, permissions, output behavior, or what 'evaluate' actually does beyond the generic verb. This is a significant gap for a tool with required state and nested question structures.
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?
The description is one short, front-loaded sentence with no redundant wording. It is concise, but it is also under-specified, sacrificing necessary context for brevity.
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?
An output schema exists, so return-value details are not strictly required, but the tool has two required parameters and nested objects. The description gives no guidance on state semantics, the structure of the question map, or logging exclusions, making it incomplete for reliable invocation.
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%, so the description must compensate. It partially clarifies that 'questions' is a named map of specific question types, but the required 'state' parameter and 'logging_exclusions' are entirely undescribed, and the term 'Noul' is unexplained.
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 uses a specific verb, 'Evaluate,' and a specific resource, 'a named map of Noul, Choice, and Score questions.' This gives the agent a reasonable sense of the tool's scope. However, 'Evaluate' is generic and the sibling tool jev_score suggests a closely related operation, so the description does not clearly differentiate among the siblings.
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 provides no guidance on when to use this tool versus alternatives like jev_check, jev_classify, or jev_score. There are no conditions, examples, or exclusions, leaving the agent without enough context to select the right tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.