ai_feature_context_pack

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It only states what it builds but does not mention side effects, permissions, rate limits, or whether it is read-only or destructive. This is insufficient for a tool with no annotations.

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 sentence that is concise and front-loaded with the key purpose. However, it could be slightly more structured (e.g., bullet points) for readability, but it is effective as is.

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 presence of an output schema (not shown) and the complexity with 7 parameters, the description is adequate but does not explain how parameters combine to produce the context. It lacks guidance on common use cases or expected results, leaving room for ambiguity.

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 parameters are fully described in the schema. The description adds the high-level concept (intent, variables, etc.) but does not clarify how parameters interact or provide additional context beyond the schema. Baseline of 3 is appropriate.

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?

Describes building AI-ready context listing specific components (intent, variables, assertions, call/read chain, graph context), making the purpose clear. However, it does not differentiate from similar sibling tools like 'feature_behavior_map' or 'call_read_deep_context', lacking distinctiveness.

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?

No guidance on when to use this tool versus alternatives. The description implies it is for building context for AI, but there is no explicit when-to-use or when-not-to-use information, nor mention of prerequisites or complementary tools.

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