get_sentiment_velocity

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

No annotations are provided, so the description carries the full burden of disclosure. It only describes the mathematical operation ('d/dt with smoothing') and access tier, but omits critical behavioral traits such as output format, latency, rate limits, authentication requirements, or whether the operation is read-only. A mutation-like operation is implied, but without annotations, the agent cannot assess safety.

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 three short sentences with no redundancy. It front-loads the key concept ('d/dt of sentiment bucket with smoothing'), then adds access tier and use case. Every sentence serves a purpose, though the structure could be improved with a clear statement of when to use.

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's complexity (2 params, no output schema), the description is too sparse. It fails to explain what the output represents (e.g., a numeric velocity value, direction, magnitude), how to interpret it for reversal detection, or any constraints like data frequency or minimum entity activity. The long sibling list does not compensate.

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 input schema covers 100% of parameters with descriptions for both 'entity_slug' (pattern, length) and 'window' (enum, default). The description adds no additional context about how these parameters influence the computation (e.g., how smoothing interacts with the window). Baseline 3 is appropriate as schema does the heavy lifting.

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 domain-specific language ('d/dt', 'sentiment bucket', 'smoothing') to convey the derivative of sentiment over time, which aligns with the tool name 'velocity'. It also mentions it's for early reversal detection, clearly differentiating it from siblings like 'get_sentiment'. The purpose is specific and actionable, though the jargon may be obscure.

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 mentions 'Pro tier' and 'Early reversal-detection signal', implying a specific use case and access level. However, it does not compare this tool to alternatives (e.g., when to use this vs. 'get_sentiment' or other sentiment tools), nor does it state when not to use it.

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