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parkyucheol-del

AlphaPipeline

by parkyucheol-del
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Python
Remote

market.cycle_compare

Compare BTC and ETH USD prices against the same day-offset in the 2016 and 2020 halving cycles, and get today's BTC dominance. Use it to gauge current cycle positioning.

Instructions

BTC/ETH's own USD price position vs the same day-offset in the 2016 and
2020 halving cycles, plus today's real BTC dominance. NOT a historical
dominance comparison (no free data source exists for that - see the
response's notice field). Derived from a cache refreshed once daily (see
scripts/collect_btc_cycle_metrics.py), so prices can be up to ~24h stale.
No input params.

Returns:
    On success: {"success": true, "btc", "eth", "btc_dominance_pct_today",
        "dxy_level", "us10y_yield_pct", "days_since_halving", ...}
    On failure: {"success": false, "error": {"type", "message"}}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.5.1

TDQS

A4.4/5.0
Behavior4/5

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: it discloses that values come from a cache refreshed once daily and can be up to ~24h stale, names the collection script, and describes both success and failure response shapes. It stops short of stating read-only status or any auth/quota expectations, which is a minor residual gap.

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

Conciseness4/5

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

Front-loaded with the core comparison, then caveats, then return shape. The script filename and enumerated return keys are slightly more detail than an agent strictly needs, but nothing is padded or redundant.

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

Completeness5/5

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

No output schema exists, so the description's return-value and error-shape summary is necessary and present, and the staleness caveat plus the notice-field pointer preempt the most likely misuse. Complete for a zero-argument read tool.

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

Parameters4/5

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

Zero parameters, so the baseline is 4; the description confirms 'No input params' and therefore nothing is left ambiguous about invocation. No further parameter meaning is needed.

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?

States a specific computation: BTC/ETH USD price position vs the same day-offset in the 2016 and 2020 halving cycles, plus today's real BTC dominance. The scope is narrow and distinctive enough to separate it from the prediction, derivatives, and DEX siblings.

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

Usage Guidelines4/5

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

Explicitly rules out the wrong interpretation ('NOT a historical dominance comparison') and points at the response's notice field for that gap. It lacks a positive 'use this when...' framing or a named alternative, but the exclusion does real routing work.

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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