chainvectorProbability

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

With no annotations, the description carries full behavioral burden. It discloses internal methodology (six-estimator convex ensemble, tick-derived 1m bars) and the semantics of kind and horizon. However, it doesn't describe the return value format or potential failure modes, which would be valuable for a probability engine.

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 dense and front-loaded with the core purpose. Each sentence adds distinct value (methodology, kind variants, horizon flexibility, use cases). Slightly long, but no redundancy; structure is logical.

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 (9 params, three kinds, no output schema), the description covers purpose, parameter interplay, and use cases thoroughly. It doesn't explicitly state the return format, but the probability semantics are well explained. Considerable effort to make the tool self-documenting.

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?

Despite 100% schema coverage, the description adds significant contextual meaning: explains kind=touch and kind=range semantics, target ladder syntax via 'targets', and how close_ts overrides horizon. This goes well beyond the schema descriptions.

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: computing probability of price hitting a target using a six-estimator ensemble. It distinguishes itself from sibling tools by specifying the probability engine nature and the different kinds (terminal, touch, range).

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?

Provides explicit use cases ('take-profit/stop placement', 'pricing any binary outcome') and explains when to use each kind and how close_ts overrides horizon. Lacks explicit exclusions or alternatives, but context is strong enough for an agent to select it appropriately.

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