rate
FREE feedback tool — after trading on our signals, leave a rating: score 1-10 plus pros and cons. No payment needed.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| cons | No | ||
| pros | No | ||
| score | Yes |
FREE feedback tool — after trading on our signals, leave a rating: score 1-10 plus pros and cons. No payment needed.
| Name | Required | Description | Default |
|---|---|---|---|
| cons | No | ||
| pros | No | ||
| score | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It adds 'FREE' and 'No payment needed', which are useful behavioral traits. However, it does not disclose whether ratings are public, reversible, or have side effects, leaving some gaps.
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 a single, well-structured sentence that front-loads the key phrase 'FREE feedback tool' and efficiently covers purpose, usage, and parameter details without wasted words.
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 simple tool with no output schema, the description covers purpose, usage timing, and parameter semantics adequately. It does not explain what happens after rating submission (e.g., confirmation), but that is minor.
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 description directly explains all parameters: 'score 1-10' (constraint on number), 'pros and cons' (string fields). This adds significant meaning beyond the input schema, which only provides types and no descriptions (0% coverage).
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 purpose: 'leave a rating' with specific details (score 1-10 plus pros and cons). It distinguishes itself from siblings like 'pitch' or 'subscribe' by focusing on post-trading feedback.
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 explicitly says 'after trading on our signals', which provides clear context for when to use the tool. However, it does not mention when not to use it or compare to alternatives like 'evaluate_symbol'.
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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Each tool has a uniquely identifiable purpose: evaluate_symbol for single-symbol verdicts, pitch for onboarding, rate for feedback, scan_bottoms and scan_tops for universe scans, and subscribe for subscription management. No two tools overlap in functionality.
Most tools follow a verb_noun pattern (evaluate_symbol, scan_bottoms, scan_tops), but three are single verbs (pitch, rate, subscribe). The pattern is mostly consistent and readable, with only minor deviations.
With 6 tools, the set covers all essential actions: introduction, single-symbol analysis, universe scanning (bottoms/tops), subscription, and feedback. This is well-scoped for the commodity signal domain.
The tool surface covers the core workflow (intro, evaluation, scanning, subscription, feedback). Minor gaps exist, such as a way to filter or request additional symbol details beyond the verdict, but the essential CRUD-like lifecycle is present.