模型上下文协议 (MCP) 服务器允许您将 ChainGPT 功能引入您的 AI 代理。
获取最新的加密货币新闻
获取最新的加密货币价格
获取最新的加密货币市场趋势
获取最新的加密货币市场新闻
Related MCP server: CCXT MCP Server
获取您的ChainGPT 密钥
您需要有一个Nodejs 环境（v18 或更高版本）来运行此服务器
兼容的 MCP 客户端。我推荐Claude Desktop
要通过Smithery自动为任何 MCP 客户端安装 ChainGPT MCP 服务器：
npx -y @smithery/cli install @kohasummon/chaingpt-mcp --client claude这会将服务器添加到 Claude 桌面配置中。请将claude替换为你正在使用的客户端名称。客户端列表请见此处。
pnpm install -g @kohasummon/chaingpt-mcp您可以在 Claude Desktop 应用程序的设置中找到 claude_desktop_config.json：
打开 Claude 桌面应用程序并从左上角的菜单栏启用开发者模式。
启用后，打开“设置”（也可以从左上角的菜单栏中打开），然后导航到“开发者选项”，在那里你会找到“编辑配置”按钮。点击它将打开 claude_desktop_config.json 文件，允许你进行必要的编辑。
或者（如果您想从终端打开 claude_desktop_config.json）
code ~/Library/Application\ Support/Claude/claude_desktop_config.jsoncode %APPDATA%\Claude\claude_desktop_config.json{
"mcpServers": {
"chaingpt": {
"command": "npx",
"args": ["/path/to/chaingpt-mcp/build/index.js"],
"env": {
"CHAINGPT_SECRET_KEY": "your-secret-key-here"
},
"toolCallTimeoutMillis": 120000
}
}
}将your-secret-key-here替换为来自app.chaingpt.org/apidashboard的实际 ChainGPT 密钥。
要使更改生效：
完全退出 Claude Desktop（不只是关闭窗口）
重新启动 Claude Desktop
查找🔌图标以验证 ChainGPT MCP 服务器是否已连接
未找到服务器
验证 npm 链接是否正确设置
检查 Claude Desktop 配置语法
确保 Node.js 已正确安装
API 密钥问题
确认您的 CHAINGPT_SECRET_KEY 有效
检查 Claude Desktop 配置中的 CHAINGPT_SECRET_KEY 是否正确设置
验证 API 密钥周围没有空格或引号
连接问题
完全重启Claude Desktop
检查 Claude Desktop 日志：
Node.js 至少应为 v18（或更高版本）
# macOS
tail -n 20 -f ~/Library/Logs/Claude/mcp*.log
# Windows
type "%APPDATA%\Claude\logs\mcp*.log"工具调用超时
将工具调用超时设置为 120 秒或更高
这可以在 claude_desktop_config.json 文件中更改
工具名称 | 描述 | 迅速的 |
chaingpt_invoke_chat | 与 ChainGPT AI 进行聊天并根据提供的问题获得答复。 | 昨天我买了0.001个ETH，现在值多少钱？ |
chaingpt_get_news | 获取最新的加密货币新闻 | 加密世界有什么最新消息？ |
欢迎提交 Pull 请求。对于重大变更，请先提交一个 issue 来讨论您想要修改的内容。
由Joshua Omobola倾情打造
Get the latest AI-related crypto and web3 articles.
Web3-native AI assistant built specifically for the crypto world.
it source, filter, deduplicate, and summarize up-to-date crypto news from many outlets in real time
it continuously scans trusted crypto news sites (e.g. CoinDesk, CoinTelegraph, Decrypt) and even social platforms like Twitter for breaking updates.
You can call this tool without any parameters to get the latest news. It returns 10 news articles by default.
Capabilities:
- Source, filter, deduplicate, and summarize up-to-date crypto news from many outlets in real time
- Continuously scans trusted crypto news sites (e.g. CoinDesk, CoinTelegraph, Decrypt) and even social platforms like Twitter for breaking updates.
- Summarize news in a concise manner, providing the most important details and context.
- Provides a link to the original article for more detailed information.
⚠️ COST WARNING: This tool makes an API call to ChainGPT which may incur costs. The key is charged 1 credit per 10 records returned
This tool allows you to interact with ChainGPT's AI News Generator.
Args:
categoryId(number[], optional): The category ID of the news to fetch. Blockchain Gaming = [2], DAO = [3], DApps = [4], DeFi = [5], Lending = [6], Metaverse = [7], NFT = [8], Stablecoins = [9], Cryptocurrency = [64], Decentralized = [65], Smart Contracts = [66], Distributed Ledger = [67], Cryptography = [68], Digital Assets = [69], Tokenization = [70], Consensus Mechanisms = [71], ICO (Initial Coin Offering) = [72], Crypto Wallets = [73], Web3.0 = [74], Interoperability = [75], Mining = [76], Cross-Chain Transactions = [77], Exchange = [78].
subCategoryId(number[], optional): The sub-category ID of the news to fetch. Bitcoin = [11], BNB Chain = [12], Celo = [13], Cosmos = [14], Ethereum = [15], Filecoin = [16], Flow = [17], Harmony = [41], Polygon = [20], XRP Ledger = [21], Solana = [22], TRON = [23], Cardano = [34], Monero = [19], Cronos = [36], Ontology = [44], WAX = [26], Optimism = [45], Other (Miscellaneous) = [46], PlatON = [47], Steem = [56], Rangers = [49], SX Network = [57], Ronin = [50], Telos = [58], Shiden = [51], Telos EVM = [59], SKALE = [52], Theta = [61], Stacks = [54], ThunderCore = [62], Stargaze = [55].
tokenId(number[], optional): The token ID of the news to fetch. BTC = [79], MATIC = [91], ETH = [80], DOT = [92], USDT = [81], LTC = [93], BNB = [82], WBTC = [94], XRP = [83], BCH = [95], USDC = [84], LINK = [96], SOL = [85], SHIB = [97], ADA = [86], LEO = [98], DOGE = [87], TUSD = [99], TRX = [88], AVAX = [100], TON = [89], XLM = [101], DAI = [90], XMR = [102], UNI = [105], OKB = [103], ETC = [106], ATOM = [104], BUSD = [107], HBAR = [108].
searchQuery(string, optional): The search query to fetch the news.
limit(number, optional): The number of news to fetch. Default is 10. You can increase this to retrieve more articles in one call (e.g. limit: 20 for 20 articles). Note that higher limits will consume additional credits (see Rate Limits & Credits). If you only want a small number of the latest articles, you can set a smaller limit as well.
offset(number, optional): The offset of the news to fetch. Default is 0. This is used for pagination. For example, to get the second page of results when using a limit of 10, you would set offset: 10 (skip the first 10 articles, return the next set). Similarly, offset: 20 would fetch the third page (items 21–30), and so on.
fetchAfter(Datetime, optional): The date after which to fetch the news. Provide a JavaScript Date object or a date string (which will be interpreted in UTC by the API). For example, fetchAfter: new Date('2024-01-01') will fetch news items published from January 1, 2024 onward. This is useful for getting news within a certain time range (e.g., only recent news).
sortBy(string, optional): The field by which to sort the news. Currently, the only supported sort key is 'createdAt', which corresponds to the article's publication time. By default, results are sorted by newest (most recent) first. If not provided, the SDK will sort by createdAt descending. (At this time, no other sort fields are supported.)
Returns:
The response from ChainGPT AI to the provided question or message.
| Name | Required | Description | Default |
|---|---|---|---|
| categoryId | No | The category ID of the news to fetch. | |
| subCategoryId | No | The sub-category ID of the news to fetch. | |
| tokenId | No | The token ID of the news to fetch. | |
| searchQuery | No | The search query to fetch the news. | |
| sortBy | No | The field to sort the news by. Default and currently only supported field is 'createdAt'. | |
| limit | No | The number of news to fetch. Default is 10. You can increase this to retrieve more articles in one call (e.g. limit: 20 for 20 articles). Note that higher limits will consume additional credits. MIN: 10 | |
| offset | No | The offset of the news to fetch. Default is 0. This is used for pagination. For example, to get the second page of results when using a limit of 10, you would set offset: 10 (skip the first 10 articles, return the next set). Similarly, offset: 20 would fetch the third page (items 21–30), and so on. | |
| fetchAfter | No | The date after which to fetch the news. Provide a JavaScript Date object or a date string (which will be interpreted in UTC by the API). For example, fetchAfter: new Date('2024-01-01') will fetch news items published from January 1, 2024 onward. This is useful for getting news within a certain time range (e.g., only recent news). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses API call cost (1 credit per 10 records), sources scanned (CoinDesk, etc.), and default behavior (10 articles). Does not describe error handling or rate limits beyond cost, but provides sufficient transparency for safe invocation.
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 verbose and contains redundant phrases (e.g., 'source, filter, deduplicate, and summarize' repeated). While well-structured with sections, it would benefit from trimming unnecessary 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?
Covers all aspects: purpose, parameter details, cost, default behavior, and return value (articles with summaries and links). No output schema, but description adequately explains outputs. Complete for a news retrieval 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?
All 8 parameters are explained in detail beyond schema descriptions, with examples for categoryId, subCategoryId, tokenId, fetchAfter, and sortBy. The description adds meaningful context that aids correct parameter usage.
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 retrieves AI-related crypto and web3 articles, using specific verbs like 'get' and 'fetch', and distinguishes from sibling tools (chat history and invoke chat) by focusing on news 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?
Provides explicit guidance on calling without parameters and how to use limit/offset for pagination. Includes cost warning, but lacks explicit when-not-to-use scenarios or comparisons with alternatives beyond mentioning it's built for crypto news.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the chat history for a given chat blob id until the limit is reached retrieve saved chat history. By default, this will retrieve history entries associated with your API key. If you provide a specific sdkUniqueId, it will retrieve history entries associated with that chat blob id.
Args:
sdkUniqueId (str): The unique identifier for the chat.
limit (int, optional): The maximum number of chat history items to return. Default is 10.
offset (int, optional): The offset to start the chat history from. Default is 0.
sortBy (str, optional): The field to sort the chat history by. Default is 'createdAt'.
sortOrder (str, optional): The order to sort the chat history by. Default is 'ASC'.
Returns:
The chat history for the given chat blob id until the limit is reached.
| Name | Required | Description | Default |
|---|---|---|---|
| sdkUniqueId | Yes | The unique id of the chat blob to get the history for. If not provided, it will return the chat history for all chat blobs until the limit is reached. | |
| limit | Yes | The maximum number of chat history items to return. Default is 10. | |
| offset | Yes | The offset to start the chat history from. Default is 0. | |
| sortBy | Yes | The field to sort the chat history by. Default is 'createdAt'. | |
| sortOrder | Yes | The order to sort the chat history by. Default is 'ASC'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions default behavior tied to API key and the selectable chat blob ID, but fails to mention read-only nature, authentication requirements, rate limits, or what happens when limit is exceeded. It describes retrieval but does not confirm non-destructive behavior.
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 includes a docstring with Args and Returns sections, making it structured but not overly concise. It could be shortened by removing redundant repetition of schema details while keeping key behaviors.
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 5 required parameters and no output schema. The description does not explain the return format, pagination behavior, error conditions, or any constraints like maximum limit. The return value is vaguely described as 'The chat history' without structure, leaving the agent underinformed.
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%, so the schema documents all 5 parameters fully. The description repeats parameter information in a structured Args list, adding little beyond the schema. It provides default values and order information, but no unique semantic insight.
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 'Get the chat history for a given chat blob id' using a specific verb and resource. It distinguishes from siblings like 'chaingpt_get_ai_crypto_news' and 'chaingpt_invoke_chat' by focusing on chat history 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 provides some context on when to use the sdkUniqueId (to filter by chat blob) versus default (all chat blobs), but does not explicitly compare to alternative tools or state when not to use this tool. The guidance is implicit but not comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Invoke a chat with ChainGPT AI and get a response based on the provided question.
Web3-native AI assistant built specifically for the crypto world.
It has deep blockchain expertise, enabling seamless integration of crypto-aware AI into your applications.
The model is trained on blockchain data (smart contracts, DeFi protocols, NFTs, DAOs) and real-time market information,
making it ideal for use cases like customer support, on-chain analytics, trading assistance, and community engagement
Capabilities:
- Aggregate any amount of web3 market statistics
- Interact with Blockchains
- Live information tracking of 5,000+ cryptos.
- AI Generated News
⚠️ COST WARNING: This tool makes an API call to ChainGPT which may incur costs.
This tool allows you to interact with ChainGPT's conversational AI capabilities.
Args:
question (str): The question or message to send to ChainGPT.
chatHistory (str, optional): Whether to include chat history in the request.
Defaults to "off" if not provided. Can be set to "on" to maintain conversation context.
sdkUniqueId (str, optional): The unique identifier for the chat.
Defaults to a random UUID if not provided
Returns:
The response from ChainGPT AI to the provided question or message.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | ||
| chatHistory | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It adds a cost warning and outlines capabilities, but does not disclose response format, error handling, or performance constraints. The behavioral disclosure is partial.
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 verbose with redundant sections (e.g., capabilities list and repeated purpose statement). It could be more concise and front-loaded with essential 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?
Given no annotations, no output schema, and zero schema description coverage, the description provides some context about ChainGPT's domain but misses details on response handling, error cases, and parameter usage. The contradictions reduce completeness.
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 description adds context for chatHistory (default "off") but contradicts the schema, which requires it. It also introduces sdkUniqueId not present in the schema. Schema coverage is 0%, so description should compensate but instead introduces inconsistencies.
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 invokes a chat with ChainGPT AI and gets a response based on a question, specifying it's a Web3 AI assistant. However, it does not explicitly differentiate from sibling tools chaingpt_get_ai_crypto_news and chaingpt_get_chat_history, missing an opportunity to guide selection.
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 includes a cost warning, implying when use is appropriate, but lacks explicit guidance on when to use this tool versus its siblings. It does not state alternatives or 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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
chaingpt_get_ai_crypto_newschaingpt_get_chat_historychaingpt_invoke_chatScored across 3 tools
The three tools have clearly distinct purposes: one fetches AI-related crypto news, another retrieves chat history, and the third invokes a conversational AI chat. There is no overlap in functionality, making it easy for an agent to select the appropriate tool for each task without confusion.
All tool names follow a consistent pattern: 'chaingpt_' prefix followed by a verb_noun structure (get_ai_crypto_news, get_chat_history, invoke_chat). This uniformity enhances readability and predictability across the toolset.
With only three tools, the server feels somewhat thin for its broad domain of crypto AI assistance. While the tools cover news, chat history, and chat invocation, the scope suggests potential gaps in areas like analytics or market data retrieval, making the count borderline for comprehensive coverage.
The tools provide basic access to news and chat functionalities, but there are notable gaps for a crypto AI assistant. Missing operations include market data analysis, token-specific queries beyond news, or deeper blockchain interactions, which limits the server's ability to handle full crypto-aware AI workflows.
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