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
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.1/5 across 2 of 2 tools scored.
The two tools have completely distinct purposes: generate_signal produces trading signals, while get_account retrieves API quota information. There is no overlap in functionality, making it impossible for an agent to confuse them.
Both tools follow a consistent verb_noun pattern (generate_signal, get_account) with clear, descriptive names. The naming convention is uniform across all tools.
With only two tools, the server feels under-scoped for a 'NeuroTrade Signal API' that implies trading signal generation and management. Key operations like updating signals, managing historical data, or configuring parameters are missing, making the toolset appear incomplete.
The toolset is severely incomplete for a trading signal API. While it covers signal generation and account status, it lacks essential operations such as listing or retrieving historical signals, updating or canceling signals, and managing trading configurations. This creates significant gaps that will hinder agent workflows.
Available Tools
2 toolsgenerate_signalAInspect
Generate an AI-powered crypto trading signal. Returns direction (OPEN_LONG / OPEN_SHORT / CLOSE), confidence score (0.0–1.0), entry price, TP levels, SL, AI thesis, technical breakdown (RSI/MACD/EMA/ATR), risk flags, and R:R ratio. Requires a valid NeuroTrade B2B API key.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Trading pair, e.g. BTC/USDT, ETH/USDT, SOL/USDT | |
| strategy | No | Strategy name: trend_rider, breakout_hunter, scalper, swing_master, macro_lens, news_reactive, momentum_surge, mean_reversion | |
| timeframe | No | Candle timeframe: 1m, 5m, 15m, 1h, 4h, 1d | 15m |
| personality | No | Signal personality shaping confidence weighting: scalper, swing, macro, trend_rider, news_reactive, breakout_hunter | |
| include_thesis | No | Include the AI narrative thesis in the response. |
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 aspects: it describes the comprehensive output structure, mentions the AI-powered nature, specifies the required API key, and indicates the tool returns actionable trading data. However, it doesn't cover rate limits, error conditions, or performance characteristics.
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 appropriately sized and front-loaded with the core purpose, followed by output details and requirements. Every sentence adds value, though the long list of output components could be slightly more structured for readability.
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 complex trading signal tool with 5 parameters, no annotations, and no output schema, the description does reasonably well by detailing the output structure and API requirement. However, it lacks information about error handling, rate limits, authentication details beyond the API key mention, and doesn't explain the relationship between parameters like 'strategy' and 'personality'.
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?
With 100% schema description coverage, the baseline is 3. The description doesn't add any parameter-specific information beyond what's already documented in the schema, though it does mention the API key requirement which isn't in the input schema parameters.
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 ('Generate an AI-powered crypto trading signal') and lists the comprehensive output components (direction, confidence score, entry price, TP levels, etc.). It distinguishes itself from the sibling tool 'get_account' by focusing on signal generation rather than account information retrieval.
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 crypto trading signal generation and mentions the prerequisite API key requirement, but provides no explicit guidance on when to use this tool versus alternatives or any exclusion scenarios. The context is clear but lacks comparative guidance.
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.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!