连接并管理其他 MCP（模型上下文协议）服务器的中心服务器。
该项目构建了一个 MCP 中心服务器，可以连接到其他 MCP 服务器，列出其工具并执行它们。它对于绕过 Cursor 40 个工具的 MCP 限制特别有用。即使在 Cursor 之外，它也可以隐藏不常用的工具，从而帮助减少 AI 错误。
Related MCP server: MCP Hub MCP Server
通过配置文件自动连接到其他 MCP 服务器
列出已连接服务器上的可用工具
调用已连接服务器上的工具并返回结果
将其添加到您的mcp.json中：
{
"mcpServers": {
"mcp-hub": {
"command": "npx",
"args": [
"-y",
"mcp-hub-mcp",
"--config-path",
"/path/to/your/mcp-hub-config.json"
]
}
}
}如果npx没有获取您的本地更改或者您希望直接运行构建的服务器，则可以按如下方式进行配置：
{
"mcpServers": {
"mcp-hub": {
"command": "node",
"args": [
"/path/to/your/mcp-hub-mcp/dist/index.js",
"--config-path",
"/path/to/your/mcp-hub-config.json"
]
}
}
}**注意：**确保您已经构建了项目（例如， npm run build或pnpm build ），以便dist/index.js存在。
Before processing a user's request, you must use the "list_all_tools" command to identify which tools are available.这确保了 AI 助手在尝试使用可用工具之前始终会检查它们。
Node.js 18.0.0 或更高版本
npm、yarn 或 pnpm
# Clone repository
git clone <repository-url>
cd mcp-hub-mcp
# Install dependencies
npm install
# or
yarn install
# or
pnpm installnpm run build
# or
yarn build
# or
pnpm buildnpm start
# or
yarn start
# or
pnpm startnpm run dev
# or
yarn dev
# or
pnpm devMCP-Hub-MCP 服务器使用 Claude Desktop 格式的配置文件自动连接到其他 MCP 服务器。您可以通过以下方式指定配置文件：
环境变量：将MCP_CONFIG_PATH环境变量设置为配置文件路径
命令行参数：使用--config-path选项指定配置文件路径
默认路径：使用当前目录中的mcp-config.json文件
配置文件格式：
{
"mcpServers": {
"serverName1": {
"command": "command",
"args": ["arg1", "arg2", ...],
"env": { "ENV_VAR1": "value1", ... }
},
"serverName2": {
"command": "anotherCommand",
"args": ["arg1", "arg2", ...]
}
}
}例子：
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/Users/username/Desktop",
"/Users/username/Downloads"
]
},
"other-server": {
"command": "node",
"args": ["path/to/other-mcp-server.js"]
}
}
}MCP-Hub-MCP 服务器提供以下工具：
list-all-tools返回所有已连接服务器的工具列表。
{
"name": "list-all-tools",
"arguments": {}
}call-tool调用特定服务器上的工具。
serverName ：调用该工具的 MCP 服务器的名称
toolName ：要调用的工具的名称
toolArgs ：传递给工具的参数
{
"name": "call-tool",
"arguments": {
"serverName": "filesystem",
"toolName": "readFile",
"toolArgs": {
"path": "/Users/username/Desktop/example.txt"
}
}
}该项目遵循常规提交来实现自动版本控制和 CHANGELOG 生成。
格式： <type>(<scope>): <description>
例子：
feat: add new hub connection feature
fix: resolve issue with server timeout
docs: update API documentation
chore: update dependencies
类型：
feat ：新功能（MINOR 版本升级）
fix ：错误修复（PATCH 版本升级）
docs ：仅文档更改
style ：不影响代码含义的改变
refactor ：既不修复错误也不添加功能的代码更改
perf ：提高性能的代码更改
test ：添加缺失的测试或更正现有的测试
chore ：对构建过程或辅助工具的更改
重大变化：在提交页脚中添加BREAKING CHANGE:以触发主要版本的提升。
Node.js
TypeScript
@modelcontextprotocol/sdk（版本：^1.9.0）
zod（版本：^3.22.4）
麻省理工学院
Call a specific tool from a specific server
| Name | Required | Description | Default |
|---|---|---|---|
| serverName | Yes | Name of the MCP server to call tool from | |
| toolName | Yes | Name of the tool to call | |
| toolArgs | Yes | Arguments to pass to the tool |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the basic action. It lacks details on behavioral traits such as error handling, permissions needed, rate limits, or what happens when calling tools (e.g., side effects, response format). This is inadequate for a tool that interacts with other servers.
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, clear sentence with zero wasted words. It's front-loaded and efficiently conveys the core purpose without unnecessary elaboration, making it easy for an agent to parse quickly.
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 complexity (calling tools across servers with nested parameters) and lack of annotations or output schema, the description is insufficient. It doesn't address critical context like how toolArgs should be structured, error cases, or what the call returns, leaving significant gaps for agent 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 description coverage is 100%, so the schema fully documents parameters (serverName, toolName, toolArgs). The description adds no additional meaning beyond the schema, such as examples or constraints, but the high coverage justifies the baseline score 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 action ('Call') and target ('a specific tool from a specific server'), which is specific and unambiguous. However, it doesn't differentiate from its sibling 'list-all-tools', which serves a completely different purpose (listing vs. calling), so it misses full sibling distinction.
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. The description merely states what it does without indicating context, prerequisites, or comparison to the sibling tool 'list-all-tools', leaving the agent to infer usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available tools from all connected servers. Before starting any task based on the user's request, always begin by using this tool to get a list of any additional tools that may be available for use.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the tool's behavior as listing tools from all servers and suggests it should be used first in tasks, which adds useful context. However, it lacks details on potential limitations (e.g., rate limits, authentication needs, or what 'all connected servers' entails), leaving some behavioral aspects unclear.
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 and well-structured in two sentences: the first states the purpose, and the second provides usage guidelines. Every sentence adds value without redundancy. It could be slightly more front-loaded by emphasizing the 'list' action first, but overall it's efficient and clear.
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 simplicity (0 parameters, no output schema, no annotations), the description is reasonably complete. It explains what the tool does and when to use it, which suffices for this context. However, it lacks details on output format or behavioral constraints (e.g., whether it's idempotent or has side effects), preventing a perfect score.
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 no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's purpose and usage. This meets the baseline of 4 for tools with no parameters, as it avoids unnecessary details.
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: 'List all available tools from all connected servers.' It specifies the verb ('list') and resource ('tools'), and distinguishes it from the sibling tool 'call-tool' by focusing on enumeration rather than invocation. However, it doesn't explicitly differentiate from potential other listing tools beyond the sibling, keeping it at 4 instead of 5.
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 explicit usage guidelines: 'Before starting any task based on the user's request, always begin by using this tool to get a list of any additional tools that may be available for use.' It clearly states when to use it (at the start of tasks) and why (to discover additional tools), offering strong guidance without alternatives needed given the context.
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.
call-toollist-all-toolsScored across 2 tools
The two tools have completely distinct purposes with no overlap: one is for calling specific tools from servers, while the other is for listing all available tools. An agent can easily tell them apart as they serve different functions in the tool management workflow.
The tool names follow a consistent verb_noun pattern (call-tool, list-all-tools) with clear actions and objects. The hyphenated style is used consistently, though 'list-all-tools' includes a modifier ('all') which is a minor deviation from a pure verb_noun structure.
With only 2 tools, the server feels thin for its apparent purpose of managing MCP tools across servers. While it covers basic operations (call and list), more comprehensive tool management (e.g., discover servers, configure connections) is missing, making the scope underdeveloped.
The tool surface is severely incomplete for managing an MCP hub. It lacks essential operations such as discovering or connecting to servers, managing server configurations, or handling errors. The two tools provide only minimal functionality, leaving significant gaps that will hinder agent workflows.
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
An MCP server that integrates with Discord to provide AI-powered features.
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