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Agent Einstein — Crypto & Market Intelligence

by io.emc2ai

Forecast Accuracy Track Record

get_forecast_accuracy
Read-onlyIdempotent

The measured, out-of-sample accuracy of Einstein's own price forecasts — MAPE and direction hit-rate per model, asset and horizon, scored after each forecast's horizon elapsed. Hit rates are computed over INDEPENDENT trials (one forecast per non-overlapping horizon window), so they are comparable to a 50% coin flip. Use this to decide how much weight to give a forecast.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assetNoRestrict to one asset symbol, e.g. BTC. Omit for the whole board; the response lists every asset that has scored rows.
limitNoMaximum scored buckets (1-50).

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description adds important interpretive context: hits are computed over independent non-overlapping trials and are comparable to a 50% coin flip. It also clarifies that scores are only available after each forecast's horizon has elapsed, which prevents misuse.

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 sentences with no filler: definitions come first, the statistical caveat second, and the use-case last. Every sentence contributes to correct selection or interpretation.

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 read-only tool with only two optional, fully described parameters and no output schema, the description covers what is returned, when to use it, and a key statistical caveat. An agent has enough context to invoke it correctly and interpret results sensibly.

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%, with both asset and limit already documented. The description does not add new parameter-level detail, so the baseline score of 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 defines what is returned: out-of-sample accuracy of Einstein's own price forecasts, with MAPE and direction hit-rate per model, asset, and horizon. The 'Einstein's own' qualifier separates this from sibling tools like get_analyst_accuracy or get_price_forecast.

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?

It explicitly tells the agent when to use the tool: 'Use this to decide how much weight to give a forecast.' It does not name alternative tools or negative cases, but the use-case guidance is clear and specific.

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

All Agent Einstein — Crypto & Market Intelligence toolsInspect
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TDQS

B3.3/5.0
Disambiguation2/5

With 40 tools, many share overlapping domains: get_smart_money_flow vs get_smart_money_inflow, scan_launchpads vs get_launchpad_radar, track_whales vs get_hyperliquid_whales, and check_token_safety vs analyze_token_security. The detailed descriptions help, but the boundaries are not always clear, making misselection likely.

Naming Consistency2/5

The tool names employ a wide variety of verbs (get_, analyze_, scan_, track_, find_, generate_, recommend_, run_, list_, ask_, assess_, detect_) with no consistent pattern. While all use snake_case, the inconsistent verb choices and occasional deviations like forecast_chart prevent predictability.

Tool Count2/5

40 tools is well above the typical 3-15 well-scoped range and exceeds the 25+ threshold for 'too many'. While the broad 'crypto intelligence' purpose justifies some breadth, the sheer number makes the surface unwieldy and suggests a lack of focused scoping.

Completeness4/5

The tool set covers a wide range of crypto intelligence domains: market analysis, forecasting, whale tracking, yield/arbitrage, security checks, prediction markets, backtesting, and even content generation. Missing operations are minor (e.g., no direct portfolio management), but core analysis and data retrieval workflows are well represented.

Resources