QuantToGo MCP is a macro-factor quantitative signal source providing AI agents access to forward-tracked trading strategies and real-time signals for US and China markets.
Core capabilities:
List strategies (list_strategies): Browse all available strategies with live performance metrics (total return, max drawdown, Sharpe ratio) — no authentication required
Get strategy performance (get_strategy_performance): Retrieve detailed data including daily NAV history for a specific strategy
Compare strategies (compare_strategies): Side-by-side comparison of 2–8 strategies across key risk/return metrics
Get index data (get_index_data): Access QuantToGo's proprietary indices — DA-MOMENTUM (China A-shares) and QTG-MOMENTUM (strategy-weighted) — with latest values and historical data
Get subscription info (get_subscription_info): View available plans and how to start a free trial
Register for trial (register_trial): Sign up for a 30-day free trial with an email address and receive an API key instantly
Get trading signals (get_signals): Retrieve timestamped buy/sell signals for a specific strategy (requires API key)
Check subscription status (check_subscription): Verify trial status, remaining days, and account details via API key
Key principles: All performance data is forward-tracked with immutable timestamps for full transparency. Signals are macro-factor driven (sentiment, FX correlations, trend timing, liquidity rotations). Users execute trades in their own brokerage accounts — zero custody risk.
Supports integration with Coze (扣子) platform via remote SSE transport, enabling AI agents to access quantitative trading signals and strategy data through the MCP protocol.
Utilizes GitHub for hosting and version control of performance data, with forward-tracked signals timestamped and immutable in git commit history for independent audit trail.
Uses GitHub Actions for automated weekly updates of strategy performance data, ensuring the performance table in the README is automatically refreshed with latest metrics.
Distributes the MCP server as an npm package, allowing installation via npx command for easy setup and deployment across different environments.
References Chinese-language technical articles on Zhihu (知乎) that explain the quantitative signal source paradigm and QTGS evaluation framework for the Chinese-speaking audience.
A macro-factor quantitative signal source accessible via MCP (Model Context Protocol). 8 tools, 1 resource, zero config. AI Agents can self-register for a free trial, query live trading signals, and check subscription status — all within the conversation. All performance is forward-tracked from live signals — not backtested.
QuantToGo is not a trading platform, not an asset manager, not a copy-trading community. It is a quantitative signal source — like a weather forecast for financial markets. We publish systematic trading signals based on macroeconomic factors; you decide whether to act on them, in your own brokerage account.
Strategy | Market | Factor | Total Return | Max Drawdown | Sharpe | Frequency |
Dip-Buying Beacon (US) | US | Sentiment: VIX panic reversal | +671.8% | -60.0% | 1.5 | Daily |
CNH-CHAU | US | FX: CNH-CSI300 correlation | +659.6% | -43.5% | 2.0 | Weekly |
Smoothed 3x Nasdaq | US | Trend: TQQQ timing | +558.3% | -69.9% | 1.4 | Monthly |
Large/Small Cap IF-IC Rotation | China | Liquidity: large/small cap rotation | +446.2% | -22.0% | 1.9 | Daily |
Smart Money CSI300 Timing | China | FX: CNY-index correlation | +385.8% | -29.9% | 1.8 | Daily |
PCR Retail Contrarian | US | Sentiment: Put/Call Ratio | +247.9% | -24.8% | 1.7 | Daily |
Unpopular Stock Contrarian | China | Attention: low-volume value | +227.6% | -32.0% | 1.5 | Monthly |
Dip-Buying Beacon (A-Share) | China | Sentiment: limit-down rebound | +81.8% | -9.1% | 1.6 | Daily |
Last updated: 2026-05-04 · Auto-updated weekly via GitHub Actions · Verify in git history
All returns are cumulative since inception. Forward-tracked daily — every signal is timestamped at the moment it's published, immutable, including all losses and drawdowns. Git commit history provides an independent audit trail.
Related MCP server: fathom-fyi
Most quantitative services fall into three categories: self-build platforms (high technical barrier), asset management (you hand over your money), or copy-trading communities (unverifiable, opaque). A signal source is the fourth paradigm:
A quant team runs strategy models and publishes trading signals
You receive the signals and decide independently whether to act
You execute in your own brokerage account — we never touch your funds
All historical signals are forward-tracked with timestamps — fully auditable
Think of it as a weather forecast: it tells you there's an 80% chance of rain tomorrow. Whether you bring an umbrella is your decision.
How to evaluate any signal source — the QTGS Framework:
Dimension | Key Question |
Forward Tracking Integrity | Are all signals timestamped and immutable, including losses? |
Strategy Transparency | Can you explain in one sentence what the strategy profits from? |
Custody Risk | Are user funds always under user control? Zero custody = zero run-away risk. |
Factor Robustness | Is the alpha source a durable economic phenomenon, or data-mined coincidence? |
{
"mcpServers": {
"quanttogo": {
"command": "npx",
"args": ["-y", "quanttogo-mcp"]
}
}
}Add to .cursor/mcp.json:
{
"mcpServers": {
"quanttogo": {
"command": "npx",
"args": ["-y", "quanttogo-mcp"]
}
}
}{
"mcpServers": {
"quanttogo": {
"url": "https://mcp.quanttogo.com/sse",
"transportType": "sse"
}
}
}https://mcp-us.quanttogo.com:8443/mcpTool | Description | Parameters |
| List all strategies with live performance | none |
| Detailed data + daily NAV history for one strategy |
|
| Side-by-side comparison of 2-8 strategies |
|
| QuantToGo custom indices (DA-MOMENTUM, QTG-MOMENTUM) |
|
| Subscription plans + how to start a free trial | none |
register_trial)Tool | Description | Parameters |
| Register a 30-day free trial with email, get API Key instantly |
|
| Get latest buy/sell signals for a strategy |
|
| Check trial status and remaining days |
|
Resource: quanttogo://strategies/overview — JSON overview of all strategies.
Ask your AI assistant:
"List all QuantToGo strategies and compare the top performers."
"I want to try QuantToGo signals. Register me with my-email@example.com."
"Show me the latest trading signals for the US panic dip-buying strategy."
"Help me register for a QuantToGo trial with email xxx@gmail.com, then show me the latest signals for US stock strategies."
Audience | URL |
Visitors / Free Trial | |
Subscribers / Invited Users | |
AI Agents / Mechanism Audit |
QuantToGo 是一个宏观因子量化信号源——不是交易平台，不是资管产品，不是跟单社区。
我们运行基于宏观经济因子（汇率周期、流动性轮动、恐慌情绪、跨市场联动）的量化策略模型，持续发布交易信号。用户接收信号后，自主判断、自主执行、自主承担盈亏。我们不触碰用户的任何资金。
类比：天气预报告诉你明天大概率下雨，但不替你决定带不带伞。
宏观因子驱动：每个策略的信号来源都有明确的经济学逻辑，不是数据挖掘
指数为主：80%以上标的为指数ETF/期货，规避个股风险
前置验证：所有信号从发出那一刻起不可篡改，完整展示回撤和亏损
零资金委托：你的钱始终在你自己的券商账户
AI原生：通过MCP协议可被任何AI助手直接调用
对你的AI助手说：
"帮我列出QuantToGo所有的量化策略，看看它们的表现。"
"帮我注册 QuantToGo 试用，邮箱 xxx@gmail.com，然后看看最新的交易信号。"
"有没有做A股的策略？最大回撤在30%以内的。"
用户类型 | 地址 |
访客 / 免费试用 | |
订阅用户 | |
AI 代理 / 机制审计 |
《量化信号源》系列文章：
宏观因子量化：为什么"硬逻辑"比"多因子"更适合信号源模式
当AI学会调用量化策略：MCP协议与量化信号源的技术实现
用AI助手获取实盘量化信号：一份实操指南
MIT
Check subscription status and remaining trial days. Requires API key from register_trial.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | Yes | Your API key from register_trial (starts with 'qtg_') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It reveals the tool checks status and remaining days and requires an API key, but does not disclose behavioral traits such as read-only nature, error handling, rate limits, or what happens with invalid keys.
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?
Two sentences with the purpose in the first sentence and a prerequisite in the second. No redundancy, efficient, and front-loaded.
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 one-parameter read tool, the description is minimally adequate but lacks information about the return value or behavior (no output schema). The agent cannot infer what the response contains, which is a gap for a complete understanding.
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% and the schema already describes the 'apiKey' parameter with detailed format hints (starts with 'qtg_'). The tool description reiterates the prerequisite but adds no new meaning beyond the schema, warranting the baseline score.
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?
Description clearly states it checks subscription status and remaining trial days, providing a specific verb and resource. However, it does not differentiate from the sibling tool 'get_subscription_info', which may have overlapping functionality.
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 gives a clear prerequisite (requires API key from register_trial) but lacks guidance on when to use this tool versus alternatives or when not to use it. No explicit exclusions or comparisons are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare multiple strategies side-by-side — returns, drawdown, and recent performance.
| Name | Required | Description | Default |
|---|---|---|---|
| productIds | Yes | Array of product IDs to compare, e.g. ['PROD-E3X', 'PROD-PCR'] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It indicates a read-like operation but does not explicitly state it is safe, require authentication, or disclose side effects. The mention of returned metrics provides some insight, but behavioral traits beyond the obvious are missing.
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 sentence that is concise and densely informative, containing the verb, resource, and key comparison dimensions. No extraneous words or repetition.
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 low complexity (one parameter) and schema coverage, the description is adequate but lacks details about the output format or structure (e.g., table, percentages) which would help an agent interpret results. No differentiation from siblings or guidance on prerequisites.
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% with a clear description for productIds. The tool description ('compare multiple strategies') adds no new semantic meaning beyond the schema, which already explains the parameter. Baseline score of 3 is appropriate.
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 verb 'Compare' and resource 'strategies', specifying the aspects compared: returns, drawdown, and recent performance. It implies side-by-side comparison, distinguishing it from single-strategy tools like get_strategy_performance, though not explicitly.
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 comparing multiple strategies, but does not provide explicit guidance on when to use this tool versus siblings like list_strategies or get_strategy_performance. No exclusion criteria or alternative tools are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get custom market indices — China A-share momentum and strategy-weighted momentum. Requires API key (get one free via register_trial).
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | Yes | Your API key from register_trial (starts with 'qtg_') | |
| indexId | No | Index ID. Omit to get summary of all indices. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description only discloses the API key requirement and its acquisition, but lacks details on error handling, rate limits, or idempotency. It does not mention that omitting indexId returns a summary (though this is in the schema). The behavioral disclosure is minimal.
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 concise, with two sentences front-loading the purpose followed by the prerequisite. It is efficient with no unnecessary words. A small addition about the summary mode would improve structure but it is otherwise well-organized.
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?
The tool has a simple input schema with no output schema. The description covers core purpose and the API key requirement, but it does not explain the two index types or the behavior when indexId is omitted. This leaves some gaps for a complete understanding, though the schema partially fills them.
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 the baseline is 3. The description adds no additional parameter meaning beyond what the schema already provides. It does not elaborate on the enum values or the summary behavior, but the schema descriptions are sufficient.
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 retrieves custom market indices, specifically China A-share momentum and strategy-weighted momentum, which differentiates it from sibling tools like get_signals or get_strategy_performance. However, it could be more explicit about the distinction between the two index types.
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 mentions the requirement for an API key and directs to register_trial, but it does not provide any guidance on when to use this tool versus alternatives (e.g., get_signals). No explicit when-not or comparison context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get today's buy/sell signals for a strategy. Requires API key from register_trial.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | Yes | Your API key from register_trial (starts with 'qtg_') | |
| productId | Yes | Strategy product ID from list_strategies, e.g. 'PROD-E3X' | |
| limit | No | Number of recent signals to return (max 50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states it is a read operation ('get') and requires an API key, which provides some behavioral context. However, with no annotations provided, it lacks details on rate limits, error handling, or data format in the response. The bar is higher without annotations, and this is insufficient for full transparency.
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, compact sentence that efficiently conveys the tool's purpose and a key prerequisite. There is no redundancy, and every word contributes value.
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?
Despite good schema coverage and conciseness, the description omits important contextual details such as the return structure, behavior when no signals exist, and error handling. For a tool with moderate complexity and no output schema or annotations, this is incomplete.
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 already has 100% description coverage for all three parameters. The description adds minimal extra meaning beyond stating the API key's origin. Thus, it meets 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 the tool retrieves today's buy/sell signals for a specific strategy. The verb 'get' and resource 'buy/sell signals' are specific, and the scope ('today's', 'for a strategy') distinguishes it from sibling tools like list_strategies or get_strategy_performance.
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 mentions a prerequisite (API key from register_trial), which is helpful. However, it does not provide guidance on when to use this tool versus alternatives like compare_strategies or get_index_data, nor does it specify when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed performance for a specific strategy — returns, drawdown, Sharpe, win rate, and daily NAV history for charting. Requires API key (get one free via register_trial).
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | Yes | Your API key from register_trial (starts with 'qtg_') | |
| productId | Yes | Strategy product ID, e.g. 'PROD-E3X' | |
| includeChart | No | Include daily NAV data points for charting |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must cover behavioral aspects. It lists specific returned metrics and notes the authentication requirement. It does not mention potential errors, rate limits, or that it is read-only, but it provides sufficient context for an agent to understand the tool's non-destructive nature.
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 extremely concise with two sentences that front-load the purpose and list outputs, then provide the authentication requirement. No wasted words.
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?
There is no output schema, so the description must explain return values. It lists key metrics but not all possible details (e.g., format of NAV data, error handling). It covers the main points but misses some completeness for a first-time user.
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 the schema already explains each parameter. The description adds value by reiterating the need for an API key and hinting at its acquisition via register_trial, but does not add significant new semantics 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 it gets performance metrics for a specific strategy, listing returns, drawdown, Sharpe, win rate, and daily NAV history. This distinguishes it from siblings like compare_strategies (compares multiple) and list_strategies (lists all).
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 mentions requiring an API key from register_trial, indicating a prerequisite. However, it does not explicitly guide when to use this tool versus alternatives like compare_strategies or get_signals, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get subscription plans and free trial info.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is minimal and does not disclose behavioral traits such as authentication requirements, response structure, or whether it is read-only. With no annotations, the burden is entirely on the description, which falls short.
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 short sentence, which is concise but could be more informative without becoming verbose.
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 parameterless info retrieval tool, the description covers the basic purpose, but lacks details about return format or data scope. Given no output schema, more context would be beneficial.
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 schema description coverage is 100%. The description does not need to explain parameters, but it could add context about what 'plans' and 'free trial info' include.
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 retrieves subscription plans and free trial info, using specific verbs and resources. However, it does not differentiate from the sibling check_subscription, which could overlap.
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?
No guidance is provided on when to use this tool versus alternatives like check_subscription, nor any context on prerequisites or usage scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available trading strategies with live performance data. Returns strategy name, market (US/China), total return, drawdown, and recent returns.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses a read-like operation (list all) and the return data. It could explicitly state it's non-destructive, but the intent is clear.
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?
Two concise sentences front-load the main action and list return fields without waste.
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 parameterless list-all tool, the description fully covers what it does and the output structure, compensating for missing 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?
No parameters exist; schema coverage is 100%. The description adds no redundancy and correctly implies no input needed.
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 lists all strategies with live performance data and specifies the output fields (name, market, returns, drawdown), distinguishing it from siblings like compare_strategies or get_strategy_performance.
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 listing all strategies but does not explicitly guide when to use this tool over alternatives or mention any context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Start a free 30-day trial. Provide your email, get an API key instantly. Calling again with the same email returns your existing account.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Your email address for registration and credential recovery |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behaviors: free trial, instant API key, idempotency on same email. It does not mention rate limits or failure modes, but for a simple registration tool, this is adequate.
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 concise with two sentences: first states the core action, second adds idempotency. No wasted words, appropriate length for a simple tool.
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 one parameter and no output schema, the description covers the main aspects: what it does, what it returns, and key behavior (idempotency). Minor gaps like error handling are acceptable given the low complexity.
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 100% description coverage on the email parameter, already explaining it's for registration and credential recovery. The tool description adds context (free trial, instant key) but does not significantly enhance parameter semantics 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 tool's purpose: to start a free 30-day trial by providing an email and receiving an API key. It distinguishes itself from siblings like check_subscription and get_subscription_info, which deal with existing subscriptions.
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 when to use (to start a trial) but does not explicitly state when not to use or mention alternatives. However, the context is clear for a registration tool, and it notes idempotency on repeat calls.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_index_data2 fields changedInput schema / properties / apiKeyAdded value: +{
+ "description": "Your API key from register_trial (starts with 'qtg_')",
+ "type": "string"
+}Input schema / requiredAdded value: +[
+ "apiKey"
+]get_strategy_performance2 fields changedInput schema / properties / apiKeyAdded value: +{
+ "description": "Your API key from register_trial (starts with 'qtg_')",
+ "type": "string"
+}Input schema / requiredPrevious value: -[
- "productId"
-]New value: +[
+ "apiKey",
+ "productId"
+]v0.2.0check_subscriptioncompare_strategiesget_index_dataget_signalsget_strategy_performanceget_subscription_infolist_strategiesregister_trialEach tool has a clearly distinct purpose: subscription management, strategy listing, performance, signals, indices, comparison, and trial registration. No overlapping functionality.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., check_subscription, get_strategy_performance, list_strategies). The verbs vary appropriately for different actions.
8 tools is well-scoped for a financial strategy platform covering subscription, strategy data, signals, indices, and comparisons. Each tool earns its place.
The tool set covers the core workflow: trial registration, subscription status, strategy listing, performance details, signals, indices, and comparison. No obvious gaps for the stated purpose.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Live macro data for AI agents: regime, conditions, liquidity, with input provenance. No signup.
Macroeconomic and FX time-series data for AI agents: indicators, calendars, COT, forex, commodities.
Prediction markets, on-chain flows, ETF flows, equities and macro intelligence for AI agents.
Build, backtest, and deploy quantitative trading strategies from your AI agent.
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