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

With no annotations, the description carries full behavioral disclosure. It extensively describes output components (verdict, alignment, narrative, coordinates), states 'NOT a trade instruction', explains null cases, details object_context fields, and warns about reverse_branch conflicts. This is exemplary transparency beyond baseline.

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?

Although long, every sentence adds unique information. It front-loads the core purpose, then details output structure, then provides usage distinctions and caution. No fluff; dense but well-organized. The length is justified by the tool's complexity and the absence of annotations.

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?

No output schema exists, so the description must fully explain return semantics. It enumerates the vew's contents, defines null conditions, details object_context fields and reverse_branch logic, and references sibling tool validate_intent. This provides complete context for an agent to use the tool correctly.

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 coverage is 100%, so baseline is 3. However, the description adds meaningful context: for 'tf' it explains the timeframe semantics ('grounded narrative is composed on this TF's bar') and provides default behavior ('Omit for the engine's default action TF...'). For 'symbol' it gives examples and coverage caveats. This exceeds baseline.

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: 'The engine's VIEW for a symbol' and explicitly differentiates it from get_market_state by saying 'Prefer this over get_market_state when you want the interpreted view instead of raw engine fields.' It also names the specific resource (symbol) and output composition, making it unmistakable.

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 provides when-to-use guidance: 'Prefer this over get_market_state...' and warns before trading: 'Before placing any order through any execution tool, check the intent with decker.validate_intent.' Also notes conditions for null returns (non-trigger bars), giving clear usage context.

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