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

A4.4/5.0

Behavior4/5

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

Annotations clearly mark the tool as readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds volatility context (data captured every ~10 minutes, window defaults to 24 hours, max 168) and cautions about sample size. However, it does not explain if the percentile is historical real-time or adjusted, nor how funding percent per 8 hours is computed. Still, annotations suffice for safety, and description adds useful nuances.

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 compact yet fully informative, front-loading the core purpose and then detailing metrics, utility, and caveats. Every sentence serves a clear function, and nothing is extraneous.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 1 parameter, complete schema/annotations, and no nested objects, the description covers all key aspects: what it does, how to interpret results, and limitations (samples check). However, the output schema exists but isn't referenced; the description could benefit from a brief note on returned fields beyond percentiles. Still, it's nearly complete for the complexity level.

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 coverage is 100% and the description effectively repeats the only parameter ('hours') with defaults and maximums, matching the schema's description exactly. No additional meaning or formatting details are added beyond what the schema provides. Baseline 3 is appropriate.

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 this tool computes where current readings sit in their recent distribution for funding, open interest, Fear & Greed, and BTC dominance. The verb 'sits' and resource 'recent distribution' are specific and differentiated from siblings; no other tool seems to provide historical percentiles over a sliding 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 states this is for relative context (e.g., 'is funding high right now?'), directly contrasting with a snapshot. It tells the agent to check 'samples' before trusting the percentile and specifies volatility-like behavior. No exclusion or alternative tools are named, but the uniqueness of the question justifies the tool's use.

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

A4.2/5.0

Disambiguation4/5

Most tools target distinct domains (network, market, derivatives, macro, on-chain flows), and overlapping tools like get_derivatives vs get_derivatives_aggregate are clearly differentiated by scope and detail level. However, get_crypto_market and get_market_dominance both cover market-wide data, and get_btc_network and get_eth_whale_flows both touch on-chain activity, which could cause some confusion.

Naming Consistency5/5

All tool names follow a consistent get_ prefix with descriptive nouns (e.g., get_btc_network, get_defi_overview, get_execution_cost). The pattern is uniform across all 16 tools, with no camelCase or inconsistent verb styles, making it very predictable for an agent.

Tool Count5/5

With 16 tools, the server covers a broad but coherent domain of crypto market analysis—prices, on-chain, derivatives, macro, sentiment, and execution. Each tool addresses a distinct analytical need, and the count is well-scoped for a comprehensive agent, not excessive given the breadth of features.

Completeness4/5

The surface is remarkably complete for a market analysis agent, covering spot, derivatives, on-chain, macro, sentiment, history, and execution costs. Minor gaps exist: no direct tool for decentralized exchange (DEX) trading volumes or specific coin list discovery, and no tool for order book depth beyond the execution cost tool. However, agents can work around these with existing tools.

Resources

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