AI-powered crypto trading signals for 400+ pairs. Generate directional signals (long/short) with TP/SL ladders, confidence scores, and AI-written trade thesis via MCP. Supports 8 proprietary strategies including Precision Hunter, Scalper, Reversal, and Breakout. Bearer token auth — free API key at neurotrade.a3eecosystem.com.
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Average 4.1/5 across 2 of 2 tools scored.
Each tool targets a distinct concern: signal generation vs account quota. There is no overlap in purpose or output.
Both tools follow a consistent verb_noun snake_case pattern (generate_signal, get_account), making their behavior predictable.
Two tools is a minimal set for a narrow API. While it covers the essential operations, the number feels thin for a full-featured service.
The core signal generation and quota checking are covered, but there is no tool for retrieving historical signals or managing signal subscriptions, which could be expected in a fuller API.
generate_signalAInspectGenerate an AI-powered crypto trading signal for a given pair and timeframe. Returns: action (OPEN_LONG | OPEN_SHORT | CLOSE), confidence (0.0–1.0), entry_price, take_profit (array of price levels), stop_loss, risk_reward ratio, indicators (rsi, macd, ema_20, atr), risk_flags (overbought_rsi | oversold_rsi | low_volume | high_spread | near_resistance | near_support), generated_at (ISO 8601), expires_at (ISO 8601), and quota_remaining. The thesis field contains LLM reasoning and is only present when include_thesis=true. On quota exhaustion returns error_code=QUOTA_EXCEEDED with Retry-After header. Requires Authorization: Bearer nt_.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Trading pair in BASE/QUOTE format, e.g. BTC/USDT, ETH/USDT, SOL/USDT. | |
| strategy | No | Signal strategy to apply. Defaults to trend_rider. | |
| timeframe | No | Candlestick timeframe for signal analysis. Defaults to 15m. | 15m |
| personality | No | Risk personality shaping confidence weighting and TP/SL aggressiveness. Defaults to scalper. | |
| include_thesis | No | When true, includes the LLM-generated reasoning in the `thesis` field of the response. Adds ~200ms latency. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the behavioral transparency burden. It discloses authentication requirements (Authorization: Bearer nt_<api_key>), detailed return structure with enums and conditionals (thesis only when include_thesis=true), error behavior (QUOTA_EXCEEDED with Retry-After header), and response expiry. This is comprehensive and goes beyond typical descriptions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense paragraph that front-loads the purpose and then enumerates return fields, error handling, and auth. Every sentence earns its place, and there is no fluff. However, the length could be streamlined with bullets for readability, so it is not maximally concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description compensates thoroughly by listing all response fields, their types/enums, conditional fields, and edge cases (quota exhaustion). It also covers auth and response expiry, ensuring the agent understands the tool's full behavior despite no structured output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all five parameters are already documented in the input schema. The description adds minimal parameter-specific value beyond mentioning 'pair and timeframe' and the include_thesis conditional. This matches the baseline of 3 for high coverage, but does not elevate it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Generate an AI-powered crypto trading signal for a given pair and timeframe.' This clearly distinguishes it from the sibling tool get_account, which handles account information. The return fields reinforce the purpose without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage via 'for a given pair and timeframe' but does not explicitly state when to use this tool versus alternatives or when not to use it. It lacks exclusions or references to sibling tools, so usage guidance is only inferred from the purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_accountAInspectReturn the current NeuroTrade B2B API quota status: plan tier, calls used, calls remaining, and quota reset date. Requires a valid NeuroTrade B2B API key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosure. It notes that a valid API key is required, which is useful, but it does not mention whether the operation is read-only, whether it consumes quota, or any potential side effects. This is basic but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that states the purpose and key requirement without extraneous words. It is highly concise and well structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and no output schema, the description adequately lists the return fields and the necessary API key. It is complete for this simple tool, though it could optionally mention quota consumption behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the baseline is 4. The description does not need to elaborate on parameter meaning, and the schema coverage is complete (100%).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb 'Return' and specifies the resource: 'current NeuroTrade B2B API quota status'. It enumerates the exact fields (plan tier, calls used, calls remaining, reset date) and is distinct from the sibling tool generate_signal.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for checking API quota but provides no explicit when-to-use guidance or comparison with alternatives. It mentions the API key requirement as a prerequisite but does not state situations where generate_signal would be more appropriate.
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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