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Agent Einstein — Crypto & Market Intelligence

by io.emc2ai

Smart Money Flow

get_smart_money_flow
Read-onlyIdempotent

Where profitable on-chain wallets have been rotating capital recently, from the tracked smart-money cohort.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows to return (1-25).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRow cap applied to every list in this payload.
staleNoTrue when the snapshot is past its refresh interval.
reasonNoWhy the payload is absent, when `available` is false.
availableNoFalse when this call has no data — a snapshot that is not warm yet, a domain switched off, or an argument that was rejected. NOT an error, and NOT a statement about the market.
fetchedAtNoWhen the underlying snapshot was refreshed (epoch ms, or ISO-8601 for the Bitcoin cycle feed). Age matters: these are cached reads, not live queries.
overallSignalNoNet read across the transfers — accumulating, distributing or neutral.
smartMoneyMovesNoOne-line summaries of the same moves.
notableTransfersNoThe transfers behind the signal.
droppedRoutineCustodyNoTransfers dropped as routine custody movement rather than signal.

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already establish that this is read-only, idempotent, and non-destructive. The description adds useful context: data comes from a tracked smart-money cohort of profitable wallets and reflects recent capital rotation. It does not disclose details like time window or inclusion criteria, but the output schema covers the return shape.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one sentence with no filler, and the core concepts of cohort and capital rotation are front-loaded. It is slightly fragmentary, but it is appropriately compact for a simple read-only tool.

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

Completeness3/5

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

For a one-parameter read-only tool with an output schema and clear annotations, the description is largely sufficient. The main gaps are the lack of a defined time window and the absence of any clarification on how this tool differs from the similar get_smart_money_inflow sibling.

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 schema has a single parameter, limit, which is fully described with a default and range. The description does not mention limit, but with 100% schema description coverage, the schema carries the semantic burden, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies the resource (profitable on-chain wallets in the tracked smart-money cohort) and the action (rotating capital), so an agent can infer this is a read about recent smart-money movement. However, it is phrased as a fragment rather than a clear verb statement, and it does not distinguish 'flow' from the sibling get_smart_money_inflow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to choose this tool over alternatives such as get_smart_money_inflow or track_whales. The word 'recently' implies temporal context, but there are no explicit use cases, exclusions, or alternative comparisons.

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