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Agent Einstein — Crypto & Market Intelligence

by io.emc2ai

Quantum Key Generation

generate_quantum_key

Generate keys with hardware quantum randomness: a post-quantum wallet, an encryption key, a signing key, or raw verified quantum random numbers. Keys are returned once and never stored. [Paid: $0.25–$2.00 per call from your Einstein credit balance. Free alternatives exist for several of these — see list_einstein_capabilities.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNowallet · encryption · signing · random (raw QRNG output).random

TDQS

A4.4/5.0
Behavior4/5

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

It adds meaningful context beyond annotations: keys are returned once and never stored, and the cost per call is disclosed. This is valuable since annotations only flag read-only/destructive hints, which are false for this tool.

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 two sentences plus a bracketed cost note, with no wasted words. It front-loads the core purpose and immediately provides key differentiators.

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?

For a single-parameter tool with no output schema, the description covers purpose, cost, storage behavior, and alternative options. It could specify the return format more precisely, but it is otherwise complete.

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 provides 100% coverage for the single 'kind' parameter, but the description enhances it with 'post-quantum' and 'hardware quantum randomness', giving more context than the schema alone. This adds value beyond the structured data.

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 generates keys using hardware quantum randomness and enumerates four specific key types (wallet, encryption, signing, random). This is a specific verb+resource pairing that distinguishes it from siblings like assess_quantum_risk.

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 notes that the tool is paid and directs users to list_einstein_capabilities for free alternatives, providing clear when-to-use guidance. It does not exhaustively explain all exclusions but gives a strong pointer.

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

B3.3/5.0
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