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LastLook Data

by io.github.zev-lll

Get Crypto Historical Prices

get_crypto_history
Read-onlyIdempotent

Returns historical daily closing prices for any supported cryptocurrency over 30, 90, or 365 days. Use for trend analysis, drawdown calculation, or training data. Source: CoinGecko. Priced at $0.15 USDC via x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
coinYesCrypto symbol e.g. BTC, ETH, SOL
daysYesHistory window: 30 ($0.15), 90 ($0.15), or 365 ($0.15) days

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
endYesEnd date (YYYY-MM-DD)
daysYesNumber of days requested
nameYesFull coin name
countYesNumber of data points returned
startYesStart date (YYYY-MM-DD)
symbolYesCrypto symbol
observationsYesDaily price observations

TDQS

A4.5/5.0
Behavior5/5

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

The description discloses the data source (CoinGecko), the exact time windows (30/90/365), and the cost ($0.15 USDC via x402), all of which go beyond the readOnlyHint, openWorldHint, and idempotentHint annotations. No contradiction 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?

Three concise, front-loaded sentences: the first states the core function, the second gives use cases, and the third provides source and pricing. Every sentence earns its place with no redundancy or fluff.

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?

The tool is simple (2 params, enums, output schema exists) and annotations cover safety. The description adds essential missing context: source, cost, and use cases. It fully equips an agent to invoke the tool correctly.

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?

Schema description coverage is 100% with enums and descriptions for both coin and days. The description mentions the days windows but does not add extra semantics beyond what the schema already provides. Baseline of 3 is appropriate when the schema does the heavy lifting.

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 historical daily closing prices for supported cryptocurrencies over 30, 90, or 365 days. The verb 'Returns' plus the resource 'historical daily closing prices' makes the purpose unambiguous and distinguishes it from siblings like get_crypto_price.

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 explicitly says 'Use for trend analysis, drawdown calculation, or training data,' which provides clear when-to-use guidance. It does not name alternative tools or state when not to use it, but the use cases are sufficient for an agent to select it appropriately.

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.

Resources