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

With no annotations provided, the description carries the full burden. It effectively discloses key behavioral traits: the 8-agent specialization, that findings include severity ratings and line numbers, and that rules are learned automatically. However, it doesn't mention performance aspects like execution time or resource requirements.

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 front-loaded with the core purpose, followed by supporting details about agents and outputs. Every sentence adds value: the first defines the action, the second lists specializations, the third describes outputs, and the fourth explains rule learning. 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?

Given the tool's complexity (multi-agent security testing) and no output schema, the description does well by specifying the attack vectors and output format (severity ratings, line numbers). However, it could better address error cases or limitations (e.g., contract size constraints).

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 100%, so the schema already documents all parameters thoroughly. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain code format or chain implications). Baseline 3 is appropriate when the schema does the heavy lifting.

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 clearly states the specific action ('Run an 8-agent security pen test') on a specific resource ('smart contract'), and distinguishes it from siblings by detailing the specialized attack vectors (reentrancy, flash loans, etc.). It goes beyond the tool name to explain the multi-agent approach and output format.

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 implies usage for smart contract security auditing but doesn't explicitly state when to use this tool versus alternatives like 'pentagonal_fix' or 'pentagonal_rules'. It mentions 'New security rules are automatically learned', which hints at iterative use, but lacks clear guidance on prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries full burden for behavioral disclosure. It describes a read-only listing operation, but doesn't mention any behavioral traits like rate limits, authentication requirements, pagination, or what happens if no blockchains are supported. The description is minimal and lacks context beyond the basic operation.

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 a single, efficient sentence that clearly states what the tool does and what information it returns. There's no wasted language or unnecessary elaboration, making it appropriately front-loaded and concise.

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 the tool's simplicity (0 parameters, no annotations, no output schema), the description is adequate but has clear gaps. It explains what the tool returns but doesn't provide context about the blockchain data's format, freshness, or limitations. For a listing tool with no structured output documentation, more detail about the return format would be helpful.

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?

The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the absence of parameters. The description doesn't need to add parameter information, and it appropriately doesn't mention any parameters. Baseline for 0 parameters with high schema coverage is 4.

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 clearly states the tool's purpose with a specific verb ('List') and resource ('all supported blockchains'), and specifies what information is returned (chain IDs, explorer URLs, deployment hints). It doesn't explicitly differentiate from sibling tools, but the specificity of listing blockchain metadata distinguishes it from tools like 'pentagonal_audit' or 'pentagonal_compile'.

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 the six sibling tools. There's no mention of alternatives, prerequisites, or specific contexts where this tool is appropriate versus others like 'pentagonal_lookup' or 'pentagonal_rules'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden. It mentions the tool compiles code and returns specific outputs (ABI, bytecode, etc.), which is helpful. However, it lacks details on error handling, performance characteristics, or any behavioral constraints like rate limits or dependencies, leaving gaps in transparency.

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 two sentences, front-loaded with the core purpose and followed by usage guidance. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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 the complexity (compilation tool with no annotations and no output schema), the description covers purpose and usage well but lacks details on output format, error cases, or integration with siblings. It's adequate for basic use but incomplete for full agent guidance without additional context.

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?

The schema description coverage is 100%, so the schema already documents both parameters ('code' and 'contract_name') fully. The description does not add any additional meaning or context beyond what the schema provides, such as examples or edge cases, meeting the baseline for high schema coverage.

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 clearly states the specific action ('Compile Solidity source code') and the resources/outputs ('return the ABI, bytecode, constructor arguments, and gas estimates'). It distinguishes from siblings like 'pentagonal_audit' by specifying this is for preparation after auditing, not for auditing itself.

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 explicitly states when to use this tool ('Use this after generating and auditing a contract to prepare it for deployment'), providing clear context and distinguishing it from alternatives like 'pentagonal_generate' or 'pentagonal_audit' without naming them directly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries full burden. It mentions the tool 'returns the complete fixed contract,' which implies a read/write operation, but doesn't disclose critical behavioral traits like whether it modifies the original code in place, requires specific permissions, handles errors, or has rate limits. For a mutation tool with zero annotation coverage, 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?

The description is a single, well-structured sentence that front-loads the purpose ('Fix a specific vulnerability in a smart contract') and efficiently covers inputs and outcomes. Every word earns its place with no redundancy or fluff.

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 the complexity of smart contract vulnerability fixing, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects (e.g., error handling, safety guarantees), output format (e.g., whether it returns only code or includes explanations), and doesn't compensate for the absence of structured fields, leaving gaps for an AI agent.

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 100%, so the schema already documents all three parameters ('code', 'finding_title', 'finding_description') with clear descriptions. The description adds no additional meaning beyond what the schema provides, such as format examples or constraints, meeting the baseline for high schema coverage.

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 clearly states the specific action ('Fix a specific vulnerability'), the resource ('smart contract'), and the outcome ('returns the complete fixed contract preserving all existing functionality'). It distinguishes itself from siblings like 'pentagonal_audit' (which likely identifies vulnerabilities) and 'pentagonal_generate' (which likely creates new contracts) by focusing on remediation.

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 implies usage when a vulnerability is known ('Provide... the finding details'), suggesting it's for post-audit fixes rather than initial auditing or generation. However, it doesn't explicitly state when not to use it (e.g., for new contract creation or without a specific finding) or name alternatives like 'pentagonal_audit' for vulnerability detection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden. It discloses that the tool generates 'production-quality' code and uses 'self-learning security rules for safer code,' which adds useful behavioral context beyond the input schema. However, it lacks details on rate limits, authentication needs, error handling, or output format (e.g., code structure), leaving gaps for a generative tool.

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 efficiently structured in two sentences: the first states the core functionality, and the second adds key features (supported chains and security rules). Every sentence earns its place by providing essential information without redundancy, making it front-loaded and appropriately sized.

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 the complexity of generating smart contracts and no output schema, the description is moderately complete. It covers the tool's purpose and key features but lacks details on behavioral aspects like error handling or output format. Without annotations, it should do more to guide the agent on usage nuances, but it suffices as a minimum viable description with clear gaps.

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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'natural language description' (aligned with 'prompt') and 'EVM (Solidity) and Solana (Anchor/Rust or SPL Token config)' (hinting at 'chain' and 'solana_type'), but it does not provide additional syntax or format details. Baseline 3 is appropriate as the schema does the heavy lifting.

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 clearly states the tool's purpose: 'Generate a production-quality smart contract from a natural language description.' It specifies the verb 'generate' and the resource 'smart contract,' and distinguishes itself from siblings like 'audit' or 'fix' by focusing on creation rather than analysis or correction. The mention of supported chains (EVM/Solana) and security rules adds specificity.

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 implies usage when a user needs to create a smart contract from natural language, but it does not explicitly state when to use this tool versus alternatives like 'pentagonal_compile' (which might compile existing code) or 'pentagonal_fix' (which might modify code). No exclusions or prerequisites are mentioned, leaving the agent to infer context from sibling tool names alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: returns comprehensive intelligence report, supports multiple blockchains (EVM and Solana), auto-detects chain if omitted, and mentions performance considerations ('Use specific fields for faster, focused queries'). However, it doesn't mention rate limits, authentication needs, or error conditions.

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 efficiently structured with zero waste. First sentence states core purpose, second lists return data, third provides usage guidance, fourth specifies blockchain support and auto-detection. Every sentence adds value and is appropriately front-loaded with the most important information.

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?

For a tool with 3 parameters, 100% schema coverage, but no output schema, the description provides good context about what data is returned and usage guidance. However, it doesn't describe the return format structure or potential limitations (like rate limits), which would be helpful given the absence of an output schema.

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 100%, so baseline is 3. The description adds meaningful context beyond the schema: it explains that the tool 'Returns the full intelligence report' and lists specific examples of what that includes (price, market cap, security flags, etc.), which helps users understand what data they'll get. It also clarifies the 'fields' parameter purpose: 'Use specific fields for faster, focused queries.'

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 clearly states the tool's purpose: 'Look up any token or smart contract by address' and specifies what it returns ('full intelligence report' with detailed examples like price, market cap, security flags). It distinguishes from siblings by focusing on lookup/analysis rather than audit, fix, or generation functions.

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 explicit usage guidance: 'Use this before auditing to understand the full token landscape.' This clearly indicates when to use this tool versus alternatives like pentagonal_audit. It also mentions 'Supports EVM and Solana tokens' and chain auto-detection, giving practical context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.

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.

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.

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.