chimera_metacognize

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

With no annotations provided, the description carries the full burden. It states the core behavior (computing ECE), but does not disclose behavioral traits such as input validation requirements, handling of empty predictions, side effects, or performance characteristics. This is insufficient for a tool with no annotation safety profile.

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 extremely concise with two sentences, no redundant phrases, and front-loads the key purpose. Every word adds value, making it efficient for an AI agent to parse.

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 lack of output schema, the description should clarify what 'overconfidence/underconfidence rates' means (e.g., dictionary of floats, thresholds). It also omits details on default values, error handling, and edge cases for empty or malformed input, leaving the agent with incomplete context for correct invocation.

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 50%, and the description adds clarity by explicitly stating the structure of 'predictions' as a list of objects with 'predicted_confidence' and 'was_correct'. However, the 'label' parameter is only described by its default in the schema, and the description adds no additional semantics for it, resulting in moderate value beyond the schema.

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 that the tool computes calibration error (ECE) from predicted confidence and correctness, and returns overconfidence/underconfidence rates. It uses a specific verb ('compute' implied) and resource (calibration error), and distinguishes from sibling tools that handle other aspects like audit or confidence.

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 does not provide any guidance on when to use this tool versus alternatives like chimera_confident or chimera_self_model. There is no mention of prerequisites, context, or exclusions, leaving the agent without decision support for tool selection.

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