NeuroTrade Signal API
Server Details
AI-powered crypto trading signals: direction, confidence, TP/SL, thesis, technicals. 8 strategies.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.4/5 across 2 of 2 tools scored.
Both tools have clearly distinct purposes: one generates trading signals and the other retrieves account quota status. There is no ambiguity or overlap.
Tool names follow a consistent verb_noun pattern (generate_signal, get_account), are descriptive, and use snake_case uniformly.
Two tools is minimal but appropriate for a focused signal API. It covers the primary action (signal generation) and account management. Could potentially expand to include signal history or configuration tools, but the count is reasonable for the stated scope.
The tool surface covers the core functionality: generating signals and checking quota. Missing features like listing past signals or managing subscriptions are gaps, but they are not essential for the primary use case.
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, the description discloses key behaviors: API key requirement, quota exhaustion with Retry-After, latency impact of include_thesis, and error codes. It does not mention rate limits or side effects, but overall transparent.
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 purpose and structured with return details and notes. It is somewhat long but each sentence adds value, making it appropriate for the tool's complexity.
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 complexity (5 params, no output schema), the description covers purpose, parameters, return fields, error handling, authentication, and latency. It is complete enough for an agent to use effectively.
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%; description adds value by explaining the return fields' relation to parameters, default behaviors, and latency note for include_thesis, exceeding the baseline of 3.
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 it generates an AI-powered crypto trading signal for a given pair and timeframe, with a lengthy list of return fields. It distinguishes itself from the sibling tool get_account by its function.
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 use when a trading signal is needed, mentions required authorization and quota handling, but does not explicitly state when not to use or provide alternatives beyond the sibling context.
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 full burden and does well by disclosing key behavioral traits: it's a read operation (implied by 'Return'), requires authentication ('Requires a valid NeuroTrade B2B API key'), and specifies the exact data returned. It doesn't mention rate limits or error behaviors, but covers the essential safety and operational context.
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: the first states the purpose and output details, the second specifies the prerequisite. Every word earns its place, and information is front-loaded with no wasted text.
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 tool's low complexity (0 parameters, no output schema, no annotations), the description is nearly complete: it covers purpose, output format, and authentication. It doesn't specify the exact return structure (e.g., JSON fields) or error cases, but for a simple quota check tool, this is adequate with minor gaps.
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 0 parameters with 100% coverage, so the baseline would be 3. The description adds value by explicitly stating there are no parameters needed ('Return the current... status' implies no inputs) and clarifies the authentication requirement, which compensates beyond the schema.
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 resource ('current NeuroTrade B2B API quota status'), listing the exact data points returned (plan tier, calls used, calls remaining, quota reset date). It distinguishes from the sibling tool 'generate_signal' by focusing on account/quota status 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 specify when NOT to use it or mention alternatives, 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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