correct_mapping

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

With no annotations provided, the description carries the full burden. It clearly states that corrections are recorded as corpus-improvement signals, feed an offline retrain, do not hot-patch the live corpus, and that noisy feedback cannot poison others. It also details the return object (ok, feedback_id, action).

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 well-structured with a main paragraph and a bulleted parameter list. It is slightly verbose (e.g., 'so every agent interaction makes normalization better' is motivational but not essential), but still efficient overall.

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 complexity (7 parameters, output schema exists), the description is remarkably complete. It covers purpose, usage context, parameter meanings, return value, and behavioral implications (offline retrain). No important gaps remain.

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?

The description adds significant meaning for 5 parameters (source_field, confirmed_canonical, original_canonical, oem, mapping_id) with clear explanations. However, two parameters (sample_value, confidence) are present in the schema but not described in the description, leaving a minor gap. Baseline for 0% schema coverage is higher, so overall it compensates well.

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 uses specific verbs ('correct', 'Teach Forge the RIGHT canonical field') and clearly distinguishes this tool from its sibling 'normalize_telemetry' by stating it is used when that tool returned wrong or abstained. The resource (canonical field mapping) is explicit.

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 explicit when-to-use conditions ('USE WHEN: you or the user can see normalize_telemetry returned the wrong canonical...') and explains the context (corrections feed an offline retrain, not live). It does not exclude alternatives but the sibling tools list shows this is unique.

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