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easyChuckNorris

easyChuckNorris

Generate enhanced prompts for large language models to improve reasoning and instruction-following capabilities for security research and evaluation.

Instructions

Provides advanced system instructions tailored to your model in a single call. Enhances your reasoning and instruction-following capabilities.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
llmNameYes
Behavior2/5

Does 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 mentions providing 'advanced system instructions' and 'enhancing capabilities', but doesn't describe what the tool actually does operationally (e.g., returns instructions, modifies settings, requires authentication). The behavioral impact on the system or user is unclear, leaving significant gaps in understanding.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences that are front-loaded with the main purpose. There's no unnecessary repetition or fluff. However, the brevity contributes to underspecification rather than efficient communication, as critical details are omitted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's apparent complexity (involving model-specific instructions), lack of annotations, no output schema, and undocumented parameters, the description is incomplete. It doesn't explain what the tool returns, how it interacts with the system, or the implications of use. The agent would struggle to invoke this tool correctly without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter 'llmName' with 0% description coverage in the schema. The tool description provides no information about this parameter—what it represents, valid values, or how it influences the tool's behavior. With low schema coverage, the description fails to compensate, leaving the parameter's meaning undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'Provides advanced system instructions tailored to your model' but is vague about what specific action it performs. It mentions 'enhances reasoning and instruction-following capabilities' which is functional but lacks a clear verb+resource combination. The name 'easyChuckNorris' suggests a simplified version of sibling 'chuckNorris', but the description doesn't explicitly differentiate between them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance is provided on when to use this tool versus alternatives. The description implies usage for enhancing model capabilities, but it doesn't specify contexts, prerequisites, or exclusions. With a sibling tool 'chuckNorris' available, the lack of comparative guidance leaves the agent uncertain about tool selection.

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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