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pentagonal_rules

Retrieve self-learning security rules accumulated from smart contract audits to enhance security detection across multiple blockchain networks.

Instructions

Get the current self-learning security rules. These rules are accumulated from every audit Pentagonal performs — the more contracts audited, the smarter the system gets.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves 'current' rules that are 'self-learning' and 'accumulated from every audit,' which hints at dynamic, updated data. However, it lacks critical details such as whether this is a read-only operation, if it requires authentication, rate limits, or what the return format looks like (e.g., JSON structure). For a tool with zero annotation coverage, this is insufficient.

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

Conciseness5/5

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

The description is highly concise and well-structured, consisting of two sentences that efficiently convey the core functionality and context. The first sentence states the purpose, and the second adds valuable background about rule accumulation. Every sentence earns its place with no wasted words, making it front-loaded and easy to understand.

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

Completeness3/5

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

Given the tool's complexity (simple retrieval with no parameters) and the absence of annotations and output schema, the description is minimally adequate. It explains what the tool does but lacks details on behavior, output format, or integration with siblings. For a tool with no structured data support, it should provide more context, such as example usage or return values, to be fully complete.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately does not discuss parameters, focusing instead on the tool's purpose. Since there are no parameters, the baseline is 4, as the description adds value without unnecessary details.

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

Purpose4/5

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

The description clearly states the tool's purpose as 'Get the current self-learning security rules' with a specific verb ('Get') and resource ('self-learning security rules'). It distinguishes the tool from siblings like 'pentagonal_audit' or 'pentagonal_fix' by focusing on retrieving accumulated rules rather than performing audits or fixes. However, it doesn't explicitly contrast with 'pentagonal_lookup' or 'pentagonal_generate', which might also retrieve information, keeping it from a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. It mentions that rules are 'accumulated from every audit Pentagonal performs,' implying a relationship with 'pentagonal_audit', but does not specify if this tool should be used before, after, or instead of other tools. There are no explicit when/when-not instructions or named alternatives, leaving usage unclear.

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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