Crypto Data & Market Analysis Agent

get_market_history

Read-onlyIdempotent

Where the current reading sits in its own recent distribution, from a series captured every ~10 minutes: funding, open interest, Fear & Greed and BTC dominance, each as current, median, min, max and percentile over the window. This is the one question a snapshot cannot answer — "is funding high right now?" has no absolute answer, only one relative to where it has been. A percentile near 0 means the reading is at the low end of the window, near 100 the high end. Check "samples" before trusting the percentile: a window with few measurements is not a distribution. Window defaults to 24 hours, maximum 168. Funding is in percent per 8 hours.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`hours`	No	How many hours back the window covers. Defaults to 24, maximum 168.

Output Schema

TableJSON Schema

Name	Required	Description
`to`	Yes
`from`	Yes
`funding`	No	Current reading placed in its own distribution over the window.
`samples`	Yes	Snapshots in the window, captured every ~10 minutes.
`fearGreed`	No	Current reading placed in its own distribution over the window.
`windowHours`	Yes
`btcDominance`	No	Current reading placed in its own distribution over the window.
`openInterestUsd`	No	Current reading placed in its own distribution over the window.

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint true, so the safe read behavior is clear. The description adds behavioral context: the sampling interval (~10 minutes), the metrics included, and the output fields (current, median, min, max, percentile). It also warns about insufficient samples, which is a key behavioral trait beyond the 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 concise and front-loaded with the core purpose. Every sentence adds value: it defines what the tool does, why it's useful, how to interpret output, and a caveat about sample size. No wasted words.

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 there is an output schema (not shown but present), the description need not explain return values. The description fully covers the tool's purpose, input parameter, behavioral traits (sampling frequency, metrics), interpretation of output (percentile meaning), and a crucial warning about data reliability. This is complete for the tool's complexity.

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 input schema has 100% description coverage, explicitly documenting the single parameter 'hours' with defaults and maximum. The description reiterates this window behavior and adds the 168 maximum. Without param gaps, baseline 3 is appropriate; the description doesn't need to add much more for this simple parameter.

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 answers where a current reading (funding, open interest, Fear & Greed, BTC dominance) sits in its own recent distribution. It differentiates itself from siblings like get_fear_greed and get_market_dominance which likely provide raw or snapshot values, while this tool uniquely provides percentiles relative to a historical window.

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?

The description explicitly explains when to use this tool: when you need to know if a reading is high or low relative to its history, stating 'the one question a snapshot cannot answer'. It also gives guidance on trusting the percentile by checking "samples" to avoid false conclusions from a small window, and specifies the window range (default 24 hours, max 168).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Crypto Data & Market Analysis Agent tools Inspect

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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.

Resources

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