NocturnusAI

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 explains key traits: it performs queries (implying read-only), uses backward chaining with unification, returns all provable answers, and optionally includes proof chains. However, it lacks details on error handling, performance implications (e.g., for complex queries), or authentication needs, leaving some gaps.

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: the first sentence defines the core purpose, followed by specifics on usage and optional features. Every sentence adds value without redundancy, making it efficient and well-structured for an AI agent.

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 (5 parameters, no annotations, no output schema), the description is adequate but incomplete. It covers the tool's purpose and key behaviors but lacks details on output format (beyond proof chains), error cases, or performance considerations. For a reasoning tool with multiple parameters, more context would be beneficial.

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: it mentions using '?-prefixed variables' for 'args' and 'full proof chains' for 'withProof', but does not provide additional syntax, examples, or constraints. This meets 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 tool's purpose: 'Query the knowledge base using multi-step logical reasoning (backward chaining with unification). Finds all provable answers by applying rules and matching facts.' It specifies the verb ('query'), resource ('knowledge base'), and method ('multi-step logical reasoning'), distinguishing it from simpler lookup tools like 'recall' or 'predicates' among 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 clear context on when to use this tool: for queries requiring logical reasoning with variables (e.g., 'Use ?-prefixed variables for unknowns you want to discover'). However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., 'recall' for simple fact retrieval), which prevents a perfect score.

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