一个 MCP（Model Context Protocol）服务器，为任何兼容 MCP 的代理 （Claude Code、Claude Desktop、Cursor、Windsurf、Codex …）提供实时加密货币市场 信号和投资组合遥测数据。
其中六个工具通过 x402 按调用付费——无需注册、 无需账户，也无需 API 密钥。 你的代理用自己的钱包在 Base 上支付几美分的 USDC， 并在同一个请求中取回数据。
claude mcp add stelar -- npx -y stelar-signals-mcp或者在任何 MCP 客户端配置中：
{
"mcpServers": {
"stelar": { "command": "npx", "args": ["-y", "stelar-signals-mcp"] }
}
}服务器在未设置任何环境变量的情况下启动，并列出所有工具。
Related MCP server: BlackSwan MCP Server
Tool | Price | 你能得到什么 |
| $0.005 | 当前价格、24 小时和 7 天涨跌、看涨/看跌/中性信号 |
| $0.005 | 情绪评分（-1 到 +1）及标签，适用于文本或资产 |
| $0.005 | AI 管理的加密货币投资组合的实时遥测——资产配置、对账后的 P&L、信号投票、人工审批记录 |
| $0.02 | 波动率 / 回撤风险状态——ATR% 与其 30 天 p80 基线的对比，标记为低/中/高 |
| $0.03 | 市场状态（震荡 / 上升趋势 / 下降趋势 / 高波动）外加网格交易适用性判定 |
| $0.05 | 基于实时 24 小时市场结构的网格数量、间距百分比、价格区间和每格分配 |
这些工具调用 https://api.stelardigital.com，该接口会以带有支付挑战的 HTTP 402 作为
响应。任何支持 x402 的客户端都会自动完成结算并重试。覆盖的资产：
SOL、BTC、ETH、XRP、DOGE、LTC、ADA、XLM。
这些是由真实市场数据支撑的线上生产端点——不是演示桩。
crypto_regime、crypto_sentiment、pricecheck、token_risk、summarize、
factcheck 通过 RapidAPI 提供。设置 RAPIDAPI_KEY 即可使用它们：
claude mcp add stelar -e RAPIDAPI_KEY=your-key -- npx -y stelar-signals-mcp没有密钥时，这六个工具会返回明确的错误；上面六个付费 x402 工具 继续正常工作，因为它们完全不使用 RapidAPI。
GET https://api.stelardigital.com/catalog endpoints, prices, parameters
GET https://api.stelardigital.com/.well-known/x402 x402 discovery manifest
GET https://api.stelardigital.com/openapi.json OpenAPI 3.1 spec一个托管的 streamable-HTTP MCP 端点也已上线，地址为
https://api.stelardigital.com/mcp。
信号覆盖上面列出的八大主流资产，而非任意代币。
付费工具需要有钱包资金；没有的话它们会返回 HTTP 402 而不是数据。
每个响应都带有 generated_at；任何内容都不会被缓存超过一分钟。
由 Stelar Digital 打造。一个人。一个 AI。一整个公司。
MIT 许可。
Market regime (chop / trend_up / trend_down / high_vol) for a major crypto pair, powered by the same classifier that steers a live production grid-trading bot. Returns a grid_suitability verdict (good/poor/etc) telling an agent whether current conditions favor range-bound (grid) strategies or trend-following ones. Use this before deciding to deploy a grid bot, mean-reversion strategy, or trend-follow strategy on SOL, XLM, BTC, ETH, XRP, DOGE, LTC, or ADA.
| Name | Required | Description | Default |
|---|---|---|---|
| asset | Yes | Asset symbol, one of: SOL, XLM, BTC, ETH, XRP, DOGE, LTC, ADA |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It mentions the tool is powered by a live production bot and returns a verdict, but does not disclose read-only behavior, rate limits, or potential side effects. Missing explicit assurance of non-destructiveness.
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 two sentences, front-loaded with the primary purpose, and contains no fluff. Every sentence contributes value, including usage guidance and output description.
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 classifier with one parameter and no output schema, the description explains the output format (regime + verdict) and supported assets. It could elaborate on how the suitability verdict is determined, but overall it is sufficient.
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 fully documents the single 'asset' parameter with an enum and description (100% coverage). The description adds no additional parameter-specific details beyond what the schema already provides, so baseline 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 tool's function: classifying market regime (chop/trend_up/trend_down/high_vol) for major crypto pairs, and returning a grid_suitability verdict. This distinguishes it from siblings like crypto_sentiment (sentiment analysis) and pricecheck (price data).
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 advises use before deploying grid bots, mean-reversion, or trend-follow strategies on a specific set of assets (SOL, XLM, etc.). It does not mention when to avoid or compare to sibling tools like crypto_sentiment, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Sentiment score (-1 to +1) with a bullish/neutral/bearish label, for either raw text or a crypto asset's recent price action, via an LLM. Give it EXACTLY ONE of text (free-form text to score) or asset (one of SOL, XLM, BTC, ETH, XRP, DOGE, LTC, ADA). Useful for agents building trading signals, news pipelines, or social-listening tools without running their own LLM sentiment call.
| Name | Required | Description | Default |
|---|---|---|---|
| text | No | Free text to score (omit if using `asset`) | |
| asset | No | Asset symbol to score sentiment for (omit if using `text`) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It mentions using an LLM and returning a score and label, but does not disclose limitations, latency, error cases, or side effects. Could be more 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 two concise sentences plus a usage instruction. No wasted words, key information front-loaded. Very efficient.
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 mentions the return format. It is complete for a simple sentiment tool, covering inputs, outputs, and usage constraints. No 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?
Schema coverage is 100%, baseline 3. The description adds clarity by explaining parameters and emphasizing mutual exclusivity ('EXACTLY ONE'), which goes 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 provides a sentiment score (-1 to +1) with a label, for either raw text or a crypto asset's recent price action. This distinguishes it from siblings like crypto_regime or factcheck.
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 says to provide exactly one of `text` or `asset` and lists valid assets. It also mentions use cases (trading signals, news pipelines). However, it lacks explicit when-not-to-use or comparison with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fact-check verdict (true / false / uncertain) with a confidence score (0-1) and a 1-2 sentence reasoning for any factual claim, via an LLM. Useful for content moderation, chatbot/agent guardrails, and misinformation screening. Returns 'uncertain' for opinions or claims requiring live/real-time data.
| Name | Required | Description | Default |
|---|---|---|---|
| claim | Yes | The factual statement to verify |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral aspects: it uses an LLM, returns a verdict with confidence and reasoning, and handles edge cases (opinions, need for real-time data). Without annotations, the description adequately informs the agent of the tool's behavior and limitations.
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—two sentences that efficiently convey purpose, output, and usage boundaries. Every sentence adds unique value without redundancy.
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 description fully covers the tool's functionality: input (a claim), output (verdict, confidence, reasoning), and special cases (returns 'uncertain' for non-factual or real-time claims). No output schema exists, but the description compensates by detailing the return structure. For a simple tool with one parameter, this is complete.
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 single parameter 'claim' is described as 'The factual statement to verify' in both the schema and description. The description adds minimal extra semantic value beyond the schema, so a baseline score of 3 is appropriate given 100% schema coverage.
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 return a verdict (true/false/uncertain), confidence score, and reasoning for a factual claim. It distinguishes itself from sibling tools (crypto_regime, crypto_sentiment, etc.) by focusing on general fact-checking rather than cryptocurrency-specific analysis.
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 lists use cases: content moderation, guardrails, misinformation screening. It also notes when the tool returns 'uncertain' for opinions or claims needing live data. However, it does not mention when to avoid using it or suggest alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Current price, 24h and 7d percent change, and a simple bullish/bearish/neutral signal for a major crypto asset. Low-latency market-data lookup for dashboards, bots, and alerts — one of SOL, XLM, BTC, ETH, XRP, DOGE, LTC, ADA.
| Name | Required | Description | Default |
|---|---|---|---|
| asset | Yes | Asset symbol, one of: SOL, XLM, BTC, ETH, XRP, DOGE, LTC, ADA |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description only mentions 'Low-latency' performance but does not disclose safety, destructive potential, or other behaviors. Burden is on description due to missing annotations.
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: first describes output, second lists use cases and assets. 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?
No output schema, but description explains return values (price, changes, signal) and use cases. Single parameter is fully documented. All necessary context provided.
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?
Input schema has 100% description coverage for the single parameter (asset enum). Description lists assets again but adds no new meaning beyond 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?
Clearly states it provides current price, 24h and 7d percent change, and a bullish/bearish/neutral signal for major crypto assets. Lists specific supported assets. Distinguishes from siblings like crypto_regime or crypto_sentiment.
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?
Explicitly mentions use cases: dashboards, bots, alerts. Lists supported assets but does not provide when-not-to-use or explicitly differentiate from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tight 3-5 sentence summary of a URL or a raw text block, via an LLM. Give it EXACTLY ONE of url (a page to fetch and summarize) or text (raw text to summarize directly). For agents/pipelines that need a fast, information-dense digest without running their own LLM call or web fetch.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Page URL to fetch and summarize | |
| text | No | Raw text to summarize directly |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses that it uses an LLM, fetches URL if provided, and outputs a 3-5 sentence summary. Lacks details on limits (e.g., text length, rate limits) but covers core behavior well.
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 zero wasted words. First sentence defines purpose and output; second gives critical usage instruction. Perfectly 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 only two parameters and no output schema, the description is largely complete. It explains what it does, how to use it, and what output to expect. Could mention error cases or size limits, but not essential for this simple tool.
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 already provides descriptions for both parameters. Description adds value by clarifying mutual exclusivity ('EXACTLY ONE') and that URL is fetched, which tells the agent how parameters interact.
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?
Clearly states the tool summarizes a URL or text via LLM, specifying output length (tight 3-5 sentences). Distinguishes from sibling tools which are in different domains (crypto, fact-checking, etc.), so purpose is unique.
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?
Explicitly instructs to provide exactly one of 'url' or 'text'. Provides context: 'For agents/pipelines that need a fast, information-dense digest without running their own LLM call or web fetch.' Does not explicitly mention when not to use, but context implies alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Volatility / drawdown risk regime (low / med / high) for a major crypto asset, extending the same classifier behind crypto_regime. Tells an agent whether current volatility is elevated vs its 30-day baseline — useful for position sizing and stop-placement logic. One of SOL, XLM, BTC, ETH, XRP, DOGE, LTC, ADA.
| Name | Required | Description | Default |
|---|---|---|---|
| asset | Yes | Asset symbol, one of: SOL, XLM, BTC, ETH, XRP, DOGE, LTC, ADA |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains the output (low/med/high regime), the basis (volatility vs 30-day baseline), and the applicability to major crypto assets. It adds context about the underlying classifier and the tool's non-destructive nature. Some limitations or edge cases could be added, but overall it provides good behavioral insight.
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 three sentences, concisely front-loading purpose, use case, and supported assets. No extraneous information; every sentence earns its place.
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 tool with one parameter and no output schema, the description explains the return value (low/med/high regime), the logic (volatility vs baseline), the supported assets, and a use case. It references the sibling tool for context, making it complete for an agent to decide whether to invoke it.
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 schema description coverage is 100%—the single 'asset' parameter is fully described with an enum. The description merely restates the supported assets ('One of SOL, XLM, BTC, ETH, XRP, DOGE, LTC, ADA'), adding no meaning beyond the schema. Baseline 3 is appropriate since the schema already handles semantics.
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 returns a volatility/drawdown risk regime (low/med/high) for a major crypto asset, specifically comparing current volatility to a 30-day baseline. It distinguishes itself from the sibling crypto_regime tool by noting it extends the same classifier but focuses on volatility risk. The verb 'tells' and resource 'risk regime' are specific.
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 provides clear context by stating the tool is 'useful for position sizing and stop-placement logic' and that it extends the classifier behind crypto_regime. This gives implicit guidance on when to use it vs. the sibling tool. However, it does not explicitly state when not to use it or provide exclusionary criteria.
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.
v1.0.0crypto_regimecrypto_sentimentfactcheckpricechecksummarizetoken_riskMost tools are distinct, but crypto_regime and token_risk both describe risk/regime for crypto assets, which could cause confusion despite different focuses (market vs. volatility). Other tools like pricecheck, sentiment, factcheck, and summarize are clearly separated.
All names use snake_case and are descriptive, but there is a mix of single-word names (factcheck, pricecheck, summarize) and crypto-prefixed names (crypto_regime, crypto_sentiment, token_risk). No major inconsistencies, but not perfectly uniform.
With 6 tools, the server is well-scoped for a crypto signals service. Each tool serves a clear purpose without being overwhelming or too sparse.
The set covers market regime, sentiment, price, risk, fact-checking, and summarization. Minor gaps exist (e.g., no on-chain data or advanced technical indicators), but the core signal needs are addressed. The inclusion of general-purpose tools (factcheck, summarize) is a slight deviation from the crypto focus.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Crypto market signals, technical indicators, and sentiment analysis for AI agents.
Market intelligence for AI agents. Real-time data, cross-market analysis, and regime detection.
Pay-per-call trading intelligence for AI agents: live market regime, trading signals, composite conv
Open API Marketplace for AI Agents. Crypto data tools with USDC payments on Base.
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