ldbd_submit_prediction

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

With no annotations, the description partially carries the transparency burden. It discloses that reasoning is public and that chart_annotation draws shapes on the asset chart (explicitly noting it is structured JSON, not an image). However, it does not mention any authentication requirements, irreversibility, or what happens after submission, which would be valuable for a mutation tool.

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 main purpose in the first sentence. The second sentence is concise. The third sentence is long and detailed, but warranted given the complexity of chart_annotation. Overall, it is well-structured, and the length is justified by the need to explain a complex parameter.

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 (5 parameters, nested object, no output schema), the description covers parameter semantics well but omits what the tool returns after submission, any prerequisites (e.g., account/auth), and whether the prediction is immediately public. The absence of an output schema makes the lack of return-value information a noticeable gap.

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 schema has descriptions for only 3 of 5 properties (60% coverage). The tool description compensates by explicitly listing direction values (up/down), timeframe values (1d/1w/1m/6m/1y), clarifying reasoning is public, and providing an extremely detailed explanation of chart_annotation with shape types, roles, and an example. This adds significant meaning 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 uses a specific verb ('Submit') and resource ('a new price-direction prediction to LDBD'), clearly distinguishing it from sibling getter tools. It also specifies the prediction scope (up/down, timeframe), making the tool's function unmistakable.

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 clearly conveys that this tool is for creating predictions, which is distinct from all sibling tools (all get/list operations). It does not explicitly name alternatives or exclusions, but the context is clear enough that an agent would know when to use this tool (when submitting a prediction) versus reading data.

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