Recompute RSI(14), ATR(14) and 1d/7d/30d performance from CoinGecko public OHLC (Wilder smoothing, daily candles) and report how far reference values (e.g. RYO's) deviate from the independent calculation.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Look-back in days (default 30, max 90) | |
| symbol | Yes | Token symbol, e.g. SOL | |
| reference_atr_14 | No | ATR(14) in USD to compare against | |
| reference_rsi_14 | No | RSI(14) to compare against |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and largely succeeds: it discloses the data source, daily candle frequency, Wilder smoothing, the recalculated metrics, and the comparison-style output. It does not cover failure modes, rate limits, or exact output formatting, but the core read-only calculation behavior is transparent.
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 dense sentence with no filler. The main action and target metrics come first, followed by the data source and the comparison behavior. Every clause earns its place.
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?
The description is sufficient for an agent to understand what the tool computes, from where, and roughly what it returns. There is no output schema, so a bit more detail about how the deviation is reported or what happens when reference values are omitted would make it fully complete, but the core invocation context is covered.
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?
Schema coverage is 100%, so the baseline is 3, but the description adds interpretive value by explaining that RSI and ATR are computed with Wilder smoothing on daily CoinGecko OHLC and that reference values are compared against this independent calculation. This helps the agent understand how days and reference parameters interrelate beyond their raw 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 states a specific operation ('recompute') and resource (RSI(14), ATR(14), 1d/7d/30d performance) with a clear data source (CoinGecko public OHLC). It also implicitly distinguishes itself from siblings like price_crosscheck by focusing on technical-indicator verification rather than price alone.
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 establishes the intended use case: independently recomputing technical indicators and measuring how far reference values drift from them. It does not explicitly state when-not-to-use or name alternatives, so it misses a top score, but the context is clear 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.