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forgequant

CoinGlass MCP Server

by forgequant

CoinGlass ETF

coinglass_etf
Read-onlyIdempotent

Track institutional crypto ETF data for Bitcoin and Ethereum to analyze market sentiment through daily flows, premiums, assets under management, and price information.

Instructions

Get crypto ETF data (Bitcoin & Ethereum).

Track institutional flows through ETFs:

  • Positive flows: Institutional buying (bullish)

  • Negative flows: Institutional selling (bearish)

Examples: - List Bitcoin ETFs: action="list", asset="bitcoin" - Daily flows: action="flows", asset="bitcoin" - IBIT premium: action="premium", ticker="IBIT"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYeslist: all ETFs | flows: daily flows | net_assets: AUM | premium: premium/discount | detail: ETF info | price: OHLC
assetNoBTC or ETH ETFsbitcoin
tickerNoETF ticker: IBIT, GBTC, ETHE
regionNoUS or Hong Kongus
intervalNoFor price: h1, d1
limitNoNumber of records

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true. The description adds useful context about institutional flow interpretation (positive/bullish, negative/bearish) which isn't captured in annotations, but doesn't disclose rate limits, authentication needs, or detailed behavioral traits beyond the basic operation.

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

Conciseness5/5

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

The description is perfectly structured: purpose statement first, key context second, then practical examples. Every sentence earns its place with zero waste. The bullet points and examples are organized for quick scanning while maintaining complete information.

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?

Given the comprehensive annotations (read-only, non-destructive, idempotent), 100% schema coverage, and existence of an output schema, the description provides exactly what's needed: clear purpose, usage context, and practical examples. No additional explanation of return values or safety concerns is necessary.

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

Parameters3/5

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

With 100% schema description coverage, the schema already documents all 6 parameters thoroughly. The description provides examples showing how parameters combine (e.g., action='list' with asset='bitcoin'), which adds some practical context, but doesn't significantly enhance the semantic understanding beyond what's in the schema.

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?

The description clearly states 'Get crypto ETF data (Bitcoin & Ethereum)' with specific verb+resource, then elaborates on tracking institutional flows. It distinguishes from siblings by focusing exclusively on ETF data, unlike other tools for funding, market data, or on-chain metrics.

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?

The description provides clear context for when to use this tool ('Track institutional flows through ETFs') and includes examples for different actions. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools for similar data.

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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