NeuroTrade Signal API
Server Details
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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.3/5 across 2 of 2 tools scored.
The two tools serve completely different purposes: generating trading signals vs checking account quota. There is no overlap or ambiguity between them.
Both tools follow a consistent verb_noun pattern (generate_signal, get_account), making it easy to understand their function at a glance.
With only 2 tools, the server is minimal but reasonably scoped for a signal generation API. However, it sits at the low end of what might be considered appropriate, as more tools could enhance utility.
The server lacks tools for common lifecycle operations such as listing historical signals, managing subscriptions, or backtesting. The current surface is insufficient for a holistic trading signal experience.
Available Tools
2 toolsgenerate_signalAInspect
Generate 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: quotas, error handling (QUOTA_EXCEEDED with Retry-After), authentication (Bearer token), latency impact of include_thesis, and response structure beyond schema. No contradictions.
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 front-loaded with the main purpose and lists return fields efficiently. While slightly verbose in enumerating fields, every sentence adds necessary detail for understanding the tool's output and behavior.
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 no output schema, the description comprehensively covers return fields, error states, authentication requirements, and parameter defaults. Context signals (5 params, 1 required, 100% schema coverage) are well-utilized, making the tool complete for a complex signal generator.
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 coverage is 100% with descriptive parameter definitions. The description adds value by explaining the return fields and the thesis condition, but does not significantly enhance parameter semantics beyond what the schema already provides.
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 clearly states the tool generates an AI-powered crypto trading signal for a given pair and timeframe, listing specific return fields. This distinguishes it from the sibling get_account, which handles account information.
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 does not explicitly provide when-to-use or when-not-to-use guidance relative to alternatives. It only implies usage through its feature list and error handling details, but lacks direct context for when to choose this over get_account.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_accountAInspect
Return 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 | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by specifying what data is returned and the authentication requirement. However, it doesn't mention potential limitations like rate limits, error conditions, or whether the data is real-time vs cached, leaving some behavioral aspects unspecified.
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 perfectly concise with two sentences that each serve distinct purposes: the first states the tool's purpose and returned data, the second states the authentication requirement. Every word earns its place with no redundancy or unnecessary information.
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 simple read-only tool with no parameters and no output schema, the description provides good coverage of what the tool does and its requirements. However, without an output schema, the description could benefit from more detail about the exact format/structure of the returned quota data, though it does list the specific data points that will be included.
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 tool has 0 parameters with 100% schema description coverage, so the baseline would be 4. The description appropriately doesn't discuss parameters since none exist, and instead focuses on the tool's purpose and requirements, which is the correct approach for a parameterless tool.
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 clearly states the specific action ('Return') and the exact resource ('NeuroTrade B2B API quota status'), listing the specific data points returned (plan tier, calls used, calls remaining, quota reset date). It distinguishes from the sibling tool 'generate_signal' by focusing on account/status retrieval rather than signal generation.
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 explicitly states when to use this tool ('Return the current NeuroTrade B2B API quota status') and includes a prerequisite ('Requires a valid NeuroTrade B2B API key'). However, it doesn't provide explicit guidance on when NOT to use it or mention alternatives to this tool, which prevents a perfect score.
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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