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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it describes the return values (APPROVED, DENIED, NEEDS_REVIEW with reasoning), mentions the 7-layer safety constraint system, and specifies domains of application. It doesn't cover rate limits or error handling, but provides substantial operational context.

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?

Two sentences that are front-loaded with core functionality, followed by usage guidance. Every sentence earns its place by providing essential information without 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?

For a validation tool with no annotations and no output schema, the description does well by explaining return values and behavioral context. It could improve by detailing error cases or system limitations, but covers the core functionality adequately given the complexity.

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 additional parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 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 tool's purpose with specific verbs ('validate an agent action against ODEI's constitutional guardrails') and resources (guardrails). It distinguishes from siblings by focusing on safety validation rather than auditing, querying, or signaling.

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?

Explicitly states when to use ('before executing any action that could affect finances, reputation, health, or relationships') and provides clear context about appropriate scenarios. No alternatives are mentioned, but the guidance is comprehensive and actionable.

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 of behavioral disclosure. It describes what the tool does (analyzes for security risks, returns a risk score and findings) and mentions performance aspects ('slower, more thorough' for 'check_holders'), which adds useful context. However, it lacks details on permissions, rate limits, error handling, or what 'actionable findings' entail, leaving gaps for a mutation-like analysis 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 appropriately sized and front-loaded, starting with the core purpose and key features. Each sentence adds value: the first defines the action and scope, the second details analysis components and output, and the third specifies supported chains. There is no wasted text, though it could be slightly more structured (e.g., bullet points for clarity).

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 (security analysis with 3 parameters) and no annotations or output schema, the description is moderately complete. It covers the purpose, analysis types, and output format (risk score and findings), but lacks details on behavioral traits like authentication needs, rate limits, or error cases. For a tool with no structured output, more elaboration on return values would be beneficial, though the description provides a basic framework.

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 ('address', 'chain', 'check_holders') with descriptions and constraints. The description adds minimal value beyond the schema, mentioning 'Supports Base and Ethereum contracts' which aligns with the 'chain' enum, and implies 'check_holders' affects thoroughness. Baseline 3 is appropriate as the schema does the heavy lifting, but the description does not add significant semantic depth.

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 ('Analyze an EVM smart contract address for security risks') and resource ('smart contract'), distinguishing it from siblings like 'odei_guardrail_check' or 'odei_world_model_query' by focusing on security auditing rather than general checks or queries. It specifies the scope (EVM contracts on Base and Ethereum) and the analysis components (verification status, vulnerability patterns, token safety, knowledge graph cross-referencing).

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 clear context for when to use this tool: for security risk analysis of EVM smart contracts on Base or Ethereum. It implies usage by specifying the supported chains and analysis types, but does not explicitly state when not to use it or name alternatives among the sibling tools (e.g., 'odei_guardrail_check' might be for different checks). The guidance is sufficient but lacks explicit exclusions or comparisons.

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 describes what the tool returns (nodes with types, domains, relationships, metadata) and the knowledge graph structure, but doesn't disclose behavioral aspects like rate limits, authentication requirements, error conditions, or whether this is a read-only operation. The description adds useful context about the knowledge graph but lacks operational 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 efficiently structured in two sentences. The first sentence establishes purpose and scope, while the second provides usage guidance. Every element serves a clear purpose with no redundant information or unnecessary elaboration.

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 search tool with 4 parameters, 100% schema coverage, but no output schema or annotations, the description provides adequate high-level context about what's being searched and why, but lacks details about return format, pagination, error handling, or performance characteristics that would be helpful 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 fully documents all 4 parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. It mentions general search capabilities but provides no additional syntax, format, or usage details for the parameters.

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 searches ODEI's constitutional world model knowledge graph and returns nodes with their attributes. It specifies the resource (10,665-node knowledge graph with 6 domains) and distinguishes this search function from potential sibling tools like guardrail checks or signal analysis.

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 clear usage contexts: 'to understand the structure of any domain, find specific entities, or explore how goals connect to execution.' However, it doesn't explicitly state when NOT to use this tool or provide direct comparisons with sibling tools like odei_world_model_signal.

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 and does well by disclosing key behavioral traits: it's a read-only operation (implied by 'scoring' and 'returns'), outputs confidence scores and evidence, and emphasizes performance traits ('faster and cheaper'). However, it lacks details on rate limits or error handling.

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 core functionality, uses two efficient sentences without redundancy, and every part (e.g., 'quick validation', 'faster and cheaper') adds value to guide usage, making it highly concise and well-structured.

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 moderate complexity (2 parameters, no output schema, no annotations), the description is mostly complete: it covers purpose, usage, and behavioral context. However, it lacks details on output format (beyond mentioning scores and evidence) and error cases, which would enhance completeness for a scoring tool.

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 both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, such as examples or usage tips for the 'category' enum, meeting the baseline for high 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 tool's purpose with specific verbs ('scoring', 'returns') and resources ('claim, entity, or topic'), distinguishing it from siblings like 'odei_world_model_query' by emphasizing speed and cost-effectiveness for quick validation rather than comprehensive analysis.

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?

It explicitly states when to use this tool ('for quick validation before making decisions') and when not to ('faster and cheaper than a full world model query'), directly comparing it to the sibling 'odei_world_model_query' as an alternative for more thorough queries.

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