$0.007 — Spot vs Hyperliquid price spread for BTC, ETH, SOL (CoinGecko spot price vs Hyperliquid perp mid). Returns spread %, direction, and viability vs 0.35% threshold.
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": "arb_spread"
}
}| 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 carries the full burden and does well on data provenance (CoinGecko spot vs Hyperliquid perp mid), the fixed asset coverage, the returned fields, and even a per-call price of $0.007. It is silent on freshness/caching, whether prices are live or sampled, and failure behavior when one venue is unavailable — material gaps for an arbitrage check where staleness changes the answer.
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?
A single dense sentence that front-loads the cost, then the computation, then the return shape — no wasted clauses. Leading with '$0.007' is slightly awkward since the operation is what an agent selects on, but it is real information, not filler.
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 zero-parameter tool with no output schema, the description covers inputs (none, fixed asset list), the computation, the return fields, and the decision threshold, which is close to sufficient. Only data-freshness and error semantics are missing, which are secondary for a read-only price comparison.
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 no parameters, so there is no per-parameter meaning to convey; the baseline applies. The description instead pins down the implicit scope — exactly three assets and a fixed 0.35% threshold — which is the useful substitute for parameter documentation here.
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 names a specific computation (CoinGecko spot price minus Hyperliquid perp mid) over a fixed, enumerated asset set (BTC, ETH, SOL), so an agent knows exactly what the tool returns. It does not name or contrast a sibling, but the scope is narrow enough that mis-selection is unlikely.
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?
Usage is implied rather than stated: the mention of a 'viability vs 0.35% threshold' tells the agent the decision this feeds, but there is no explicit when-to-use, when-not-to-use, or alternative named among the many sibling signal tools. An agent must infer that this is the spot/perp basis check rather than a general market snapshot.
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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