Agent Einstein — Crypto & Market Intelligence

AI Chart Forecast

forecast_chart

Read-onlyIdempotent

Real-time data retrieval and compute for any token, on any supported chain — bundles a live dual-model price forecast (Google TimesFM 3.0 + Kronos, with quantile confidence bands) at no separate charge. Unlike the free get_price_forecast this runs fresh inference and is not limited to four assets. Experimental research feature — not investment advice; use at your own risk. [Paid: $0.65 per call from your Einstein credit balance. Free alternatives exist for several of these — see list_einstein_capabilities.]

Input Schema

TableJSON Schema

Name	Required	Description	Default
`chain`	No	Blockchain network.	base
`token`	Yes	Token symbol or contract address to forecast.
`timeframe`	No	Candle interval for the forecast.	1d

Output Schema

TableJSON Schema

Name	Required	Description
`skill`	No	Which Einstein capability answered. These tools route onto one of many skills by an enum argument, so this names the branch that actually ran.
`reason`	No	Why there is no analysis, when `available` is false.
`analysis`	No	The written answer, identical to the result's text block. This is the field to read: the rest of the payload is whichever capability answered, and its shape varies by tool and by argument.
`available`	No	True when the analysis ran. False when it did not — no capability matched the arguments, the caller is out of credit, billing was unavailable, or the skill produced nothing. NOT a statement about the market.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 4, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "analysis": {
+      "description": "The written answer, identical to the result's text block. This is the field to read: the rest of the payload is whichever capability answered, and its shape varies by tool and by argument.",
+      "type": "string"
+    },
+    "available": {
+      "description": "True when the analysis ran. False when it did not — no capability matched the arguments, the caller is out of credit, billing was unavailable, or the skill produced nothing. NOT a statement about the market.",
+      "type": "boolean"
+    },
+    "reason": {
+      "description": "Why there is no analysis, when `available` is false.",
+      "type": "string"
+    },
+    "skill": {
+      "description": "Which Einstein capability answered. These tools route onto one of many skills by an enum argument, so this names the branch that actually ran.",
+      "type": "string"
+    }
+  },
+  "type": "object"
+}

AddedAug 17, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses that this is an experimental research feature, not investment advice, and that each call costs $0.65 from the user's Einstein credit balance. It also clarifies the bundled nature of the forecast, adding meaningful behavioral context.

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 dense but every sentence earns its place: purpose, differentiation, risk warning, and pricing are all covered without filler. Key differentiators are front-loaded early in the description.

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?

With full schema coverage, an output schema present, and annotations covering safety and idempotency, the description fills the remaining gaps: cost, experimental status, free alternatives, and comparison to a sibling. An agent has enough contextual information to decide whether and how to invoke this tool.

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 description coverage is 100%, so the schema already documents token, chain, and timeframe with clear descriptions and enums. The description adds 'any token' and 'not limited to four assets,' but does not significantly deepen parameter-level understanding beyond what the schema already provides.

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 what the tool does: real-time data retrieval and compute bundled with a live dual-model price forecast for any token on any supported chain. It also explicitly distinguishes itself from the sibling get_price_forecast by noting it runs fresh inference and is not limited to four assets.

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 contrasts this tool with get_price_forecast, telling agents this should be used when fresh inference or coverage beyond four assets is needed. It also points to list_einstein_capabilities for free alternatives and discloses the paid nature of the call, giving clear selection guidance.

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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