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defog-ai
by defog-ai

get_results

Retrieve predictions, per-metric errors, and a leaderboard for AI agent configurations used in equity earnings forecasting.

Instructions

Return predictions, per-metric errors, and the configuration leaderboard.

Lower symmetric mean absolute percentage error (SMAPE) is better. Set settled_only=false to include predictions whose actuals are not recorded yet.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
modelNo
tickerNo
harnessNo
settled_onlyNo
thinking_settingNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, description explains output content and SMAPE meaning, but does not cover edge cases, pagination, or auth requirements. Adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two focused sentences with no filler. First sentence states main output, second adds key insight.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters and output schema, description covers main output but omits parameter semantics for most fields, leaving some gaps for agent usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only settled_only is explained in description; 0% schema coverage leaves 5 parameters unexplained. Fails to compensate for lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states verb (return) and resources (predictions, per-metric errors, leaderboard), distinguishing from sibling tools which record data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implicitly suggests usage for retrieving results vs recording siblings, but lacks explicit when-to-use or alternatives. Provides parameter guidance for settled_only.

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

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