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

With no annotations provided, the description fully carries the transparency burden. It discloses return contents (ic_ratio per horizon, drift status, plain-English narrative), provides actionable thresholds (ic_ratio < 0 = sign flip and halt new positions, < 0.3 = severe/retrain, > 1.0 = outperforming), and mentions performance ('Sub-second'). This gives the agent a clear behavioral model.

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 front-loaded with the core concept and 'CALL THIS' guidance, then gives return fields and thresholds. Each sentence adds distinct value—definition, use cases, return structure, frequency, and interpretive thresholds—without redundant fluff. It is dense but efficient.

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?

For a zero-parameter status tool with no output schema, the description is exceptionally complete. It explains the metric, what is returned, how to interpret the values, and the refresh cadence. The agent can confidently invoke this tool and interpret results without additional context.

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 tool has 0 parameters, so the baseline is 4. The empty schema fully documents that there are no inputs. The description adds no parameter-specific detail because none is needed; it focuses on the output and interpretation, which aligns with the 0-param 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 function: 'Live IC drift status — comparison of realised live IC vs training-time IC.' It identifies the resource (IC drift) and the specific comparison, and positions it as 'the canonical alpha-decay early warning,' distinguishing it from related tools like voter_ic_drift. The use-case phrases ('is the model still working?') further pin down its intent.

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 gives explicit trigger phrases: 'CALL THIS when the user asks...' and lists three concrete user intents. It also notes refresh cadence ('refreshed daily after the close'), which helps the agent decide if the data is fresh enough. However, it does not explicitly mention when not to use it or point to alternative tools (e.g., voter_ic_drift), so it lacks exclusion guidance.

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