$0.007 — RSI-based mean reversion scan across BTC, ETH, SOL, AVAX, LINK. Flags oversold/overbought assets with reversion signals active in RANGING and CHOPPY regimes.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "mean_reversion_scan"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears the full disclosure burden. It helpfully reveals the per-call price ($0.007), that the asset set is fixed (no parameters to target assets), and the regime filter that gates when signals are reported. It does not disclose the response shape, whether results are a live snapshot or historical, refresh cadence, or what 'reversion signals active' concretely emits, leaving real behavioral 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?
Two tight sentences with the price front-loaded and the method, universe, and signal following immediately; every clause carries information. The leading cost prefix is unconventional but relevant for a paid tool, though it competes with the actual purpose for the most prominent position.
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?
Since there are no parameters, no annotations, and no output schema, the description is the only documentation available. It covers method, universe, and regime applicability, but omits the return format and whether the scan is real-time or periodic, which an agent needs to decide how to consume the result.
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 tool takes zero parameters, so the baseline is 4; there is no parameter meaning for the description to add. The description does usefully clarify that the five named assets are a fixed scope rather than selectable input, which prevents an agent from hunting for a ticker argument.
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?
States a specific verb and method (RSI-based mean reversion scan) plus the exact asset universe (BTC, ETH, SOL, AVAX, LINK) and the output signal (oversold/overbought flags). It is clear what the tool does, but it never contrasts itself with the many other signal tools in the sibling list (e.g. bb_analysis, dca_signal, apex_alpha_score), so differentiation is left to the agent.
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 phrase 'active in RANGING and CHOPPY regimes' gives an implicit usage condition: this scan is relevant in range-bound/choppy markets. However, it never states what to use in trending conditions, never names an alternative, and never gives an explicit 'do not use when' rule, so the agent must infer the boundary.
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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