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Glama

Crypto Data & Market Analysis Agent

get_derivatives_aggregate

Read-onlyIdempotent

Watchlist sweep over up to 10 coins in one call: the headline funding and open interest figures per coin — average funding, total open interest in USD, funding spread — with no per-venue rows, which keeps the response compact enough to compare positioning across a basket. Costs a single upstream call no matter how many coins you request, so prefer it over repeated single-coin calls. Figures come from the major liquid venues — Binance, Bybit, OKX and Hyperliquid; thin venues are excluded, since their outlier rates would otherwise distort the average by an order of magnitude. Coins with no major-venue data are listed in "unavailable" instead of failing the whole request. Reach for get_derivatives instead when you need the per-venue detail on one coin. Funding is returned in PERCENT per 8 hours (0.0061 means 0.0061%, not 0.61%) and also ANNUALIZED as a percent per year — 0.0067% per 8h is 7.3% a year, which says immediately whether a long is paying more than a bond yields. Open interest is in USD. Current snapshot.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinsNoBase coins, e.g. ["BTC","ETH","SOL"]. Maximum 10. Defaults to BTC and ETH.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinsYes
unavailableYesRequested coins with no major-venue perpetual. Never silently dropped.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations declare readOnly, openWorld, idempotent, and non-destructive, and the description adds valuable context beyond these: venue selection (major liquid venues only), fallback behavior (coins listed in 'unavailable' instead of failing), and unit conventions (funding in percent per 8h and annualized). No contradictions with annotations.

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 internally structured and front-loaded: purpose, efficiency, data-source rationale, fallback behavior, sibling distinction, and unit clarification. Every sentence earns its place, including the concrete annualization example, making it informative without waste.

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 tool's moderate complexity and the presence of an output schema, the description covers all key aspects: data sources, units, fallback behavior, and the distinction from get_derivatives. It explains return values enough ('unavailable' list, aggregate figures) and notes 'Current snapshot' for time sensitivity.

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?

The schema already fully describes the only parameter (coins) with max 10 and defaults, so the baseline is 3. The description adds little beyond schema, only implying the output groups by coin. It does not introduce new parameter constraints or format details.

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 it provides 'headline funding and open interest figures per coin' for up to 10 coins, with specific components (average funding, total OI, funding spread). It explicitly differentiates from sibling get_derivatives by noting 'no per-venue rows' and directing users to the sibling for detail.

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

Usage Guidelines5/5

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

Explicit guidance is given: 'Costs a single upstream call... so prefer it over repeated single-coin calls' and 'Reach for get_derivatives instead when you need the per-venue detail on one coin.' This clearly states when to use this tool and when to use an alternative.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct data domain—network health, market prices, news, DeFi, derivatives per-venue vs aggregate, economic calendar, ETH address, whale flows, execution cost, sentiment, implied volatility, macro rates, market brief, dominance, history, and traditional market quotes. The only similar pair (get_derivatives vs get_derivatives_aggregate) is clearly differentiated by level of detail, so there is no real ambiguity.

Naming Consistency5/5

All tools follow the get_<domain> pattern with descriptive noun phrases (e.g., get_btc_network, get_eth_whale_flows, get_market_brief). No mixed verb styles or casing conventions appear, making the naming fully predictable and consistent.

Tool Count4/5

At 17 tools, the surface is slightly above the ideal 3-15 range, but the breadth of the domain—spot, derivatives, on-chain, macro, sentiment, and execution—justifies each tool. It's borderline but each tool earns its place in a comprehensive market analysis agent.

Completeness5/5

The toolset covers nearly every major facet of crypto market analysis: prices, history, dominance, derivatives, on-chain activity, DeFi, macro, economic calendar, sentiment, implied volatility, execution cost, and a composite brief. Minor gaps like historical OHLCV or multi-chain on-chain analytics are not critical given the agent's stated focus.

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