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LastLook Data

by io.github.zev-lll

Get Crypto Top 20 Bundle

get_bundle_crypto
Read-onlyIdempotent

Returns the top 20 cryptocurrencies by market cap in one call: price, 24h change, 7d change, market cap, and volume. Covers BTC, ETH, SOL, BNB, XRP, USDT, USDC, ADA, AVAX, DOGE, and more. Use this instead of individual get_crypto_price calls when you need broad market coverage. Source: CoinGecko. Priced at $0.50 USDC via x402 on Base.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofYesISO timestamp of the data fetch
coinsYesTop 20 coins by market cap
countYesNumber of coins returned
bundleYesBundle identifier: crypto

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable context: the call is a single bundled request, sourced from CoinGecko, and costs $0.50 USDC via x402 on Base, which are important operational details not present in annotations.

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?

The description is efficient and front-loaded with the primary action. It includes an explicit usage pointer and source/pricing details in a compact form. However, it could be slightly more concise by merging the source and price sentences, but it remains readable.

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 output schema exists and annotations cover safety, the description provides all necessary selection context: what data it returns, when to use it, the data source, and cost. The sibling tools are different domains, and the description names the specific alternative (get_crypto_price), making the choice clear.

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?

The tool has zero parameters, so the schema already fully covers inputs (schema coverage 100%). The description adds meaning by describing what data will be returned (price, 24h/7d change, market cap, volume) and the coin list, which is useful given no parameters.

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 starts with 'Returns the top 20 cryptocurrencies by market cap in one call' which names a specific verb, resource, and scope. It also lists the exact data fields (price, 24h change, 7d change, market cap, volume) which distinguishes it from sibling tools like get_crypto_price and other bundles.

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?

Explicitly says 'Use this instead of individual get_crypto_price calls when you need broad market coverage.' This provides a clear when-to-use and names the alternative, making it easy for an agent to choose this tool over get_crypto_price.

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

A3.9/5.0
Disambiguation3/5

The tool set has several overlapping functions, particularly among the bundle tools (e.g., get_bundle_context_brief vs get_bundle_macro both provide macro indicators, and get_bundle_rate_environment overlaps with get_yield_curve and get_policy_spread). Individual current/history/date tools are distinct but some redundancy exists (e.g., get_treasury_yield_current vs get_current_value for DGS30). Descriptions help differentiate purposes, but agents may still hesitate when selecting between similar bundles.

Naming Consistency4/5

All tool names begin with the verb 'get_' and use snake_case, creating a consistent pattern. The bundle tools are uniformly prefixed with 'get_bundle_', and individual data tools follow a get_[entity]_[modifier] structure (e.g., get_fx_rate_current, get_fx_rate_by_date, get_fx_rate_series). Minor deviations exist (e.g., get_series, get_current_value, get_value_by_date are less descriptive of the underlying entity), but overall the naming is predictable and readable.

Tool Count3/5

With 24 tools, the server is on the heavy side of the typical range. The broad domain (macro data, mortgage, crypto, FX, treasury, EDGAR) justifies many tools, but some could potentially be consolidated (e.g., individual rate tools vs rate bundles). The count does not feel overwhelming, but it is borderline heavy.

Completeness4/5

The server covers a wide range of economic and financial data with both bundled and granular views. It includes current, historical, and date-specific retrievals for FRED series, FX, and crypto, plus specialized tools for mortgages, recession, and yield curves. Minor gaps exist, such as no way to list all supported FRED series or full financial statements for EDGAR, but the core lifecycle of data querying is well covered.

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