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LiveDataLink

by io.github.blackboxfoundry

crypto_price

Read-onlyIdempotent

Get the current price and market data for any cryptocurrency. Returns price in USD, 24-hour change, market cap, volume, and all-time high. Use this for 'what's the price of bitcoin?', 'how much is ethereum?', 'solana price', 'check dogecoin', 'BTC price', 'ETH value', 'crypto price check', or any question about a specific coin's current value. Supports all major cryptocurrencies: bitcoin, ethereum, solana, cardano, ripple/XRP, dogecoin, polkadot, avalanche, chainlink, polygon/MATIC, litecoin, uniswap, stellar, cosmos, NEAR, arbitrum, optimism, aptos, sui, toncoin, shiba inu, pepe, BNB, tether/USDT, USDC, and thousands more via CoinGecko ID.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinYesCryptocurrency name or ticker (e.g., 'bitcoin', 'BTC', 'ethereum', 'ETH')

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already establish readOnly, openWorld, idempotent, and non-destructive behavior, so the bar for disclosure is lower. The description adds valuable behavioral context by listing the exact data fields returned and emphasizing broad coin support via CoinGecko IDs, which helps set expectations beyond the structured 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 core purpose and return fields are front-loaded in the first sentence, with usage examples following. The long list of supported coins is somewhat excessive but relevant, helping agents understand the tool's broad coverage without opening external documentation.

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?

For a simple one-parameter quote tool with no output schema, the description is complete: it states what is returned, how to invoke it, what inputs are accepted, and the range of supported coins. Nothing essential for correct invocation is missing.

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 schema already fully documents the single 'coin' parameter with examples, so the baseline is 3. The description adds extra semantic value by showing many accepted forms (names, tickers like BTC/ETH, and CoinGecko IDs), making the accepted input format more concrete and actionable.

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 the tool returns current price and market data for any cryptocurrency, naming the specific fields (price, 24-hour change, market cap, volume, all-time high). It includes concrete query examples that distinguish it from related siblings like crypto_compare and crypto_trending.

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 explicit use context ('Use this for...'), with many natural-language examples and a clear scope: questions about a specific coin's current value. It does not explicitly name alternative tools or when not to use it, but the guidance is sufficiently clear for an agent to decide.

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

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources