他の MCP (Model Context Protocol) サーバーに接続して管理するハブ サーバー。
このプロジェクトは、他のMCPサーバーに接続し、ツールをリストアップして実行できるMCPハブサーバーを構築します。これは特に、Cursorの40ツールというMCP制限を回避するのに役立ちます。Cursor外でも、使用頻度の低いツールを非表示にすることでAIのミスを減らすのに役立ちます。
Related MCP server: MCP Hub MCP Server
設定ファイルによる他のMCPサーバーへの自動接続
接続されたサーバー上で利用可能なツールを一覧表示する
接続されたサーバー上のツールを呼び出し、結果を返す
mcp.jsonに以下を追加します:
{
"mcpServers": {
"mcp-hub": {
"command": "npx",
"args": [
"-y",
"mcp-hub-mcp",
"--config-path",
"/path/to/your/mcp-hub-config.json"
]
}
}
}npxローカルの変更を取得していない場合、またはビルドしたサーバーを直接実行したい場合は、次のように設定できます。
{
"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: dist/index.jsが存在するように、プロジェクトをビルドしたことを確認してください (例: npm run buildまたはpnpm build )。
Before processing a user's request, you must use the "list_all_tools" command to identify which tools are available.これにより、AI アシスタントは、ツールを使用する前に常に利用可能なツールを確認するようになります。
Node.js 18.0.0以上
npm、yarn、または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サーバーは、Claude Desktop形式の設定ファイルを使用して他のMCPサーバーに自動的に接続します。設定ファイルは、以下の方法で指定できます。
環境変数: MCP_CONFIG_PATH環境変数を設定ファイルのパスに設定します
コマンドライン引数: --config-pathオプションを使用して設定ファイルのパスを指定します
デフォルトパス: 現在のディレクトリのmcp-config.jsonファイルを使用します
設定ファイルの形式:
{
"mcpServers": {
"serverName1": {
"command": "command",
"args": ["arg1", "arg2", ...],
"env": { "ENV_VAR1": "value1", ... }
},
"serverName2": {
"command": "anotherCommand",
"args": ["arg1", "arg2", ...]
}
}
}例：
{
"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 サーバーは次のツールを提供します。
list-all-tools接続されているすべてのサーバーからのツールのリストを返します。
{
"name": "list-all-tools",
"arguments": {}
}call-tool特定のサーバー上のツールを呼び出します。
serverName : ツールを呼び出すMCPサーバーの名前
toolName : 呼び出すツールの名前
toolArgs : ツールに渡す引数
{
"name": "call-tool",
"arguments": {
"serverName": "filesystem",
"toolName": "readFile",
"toolArgs": {
"path": "/Users/username/Desktop/example.txt"
}
}
}このプロジェクトは、自動バージョン管理と CHANGELOG 生成のためにConventional Commitsに従います。
形式: <type>(<scope>): <description>
例:
feat: add new hub connection feature
fix: resolve issue with server timeout
docs: update API documentation
chore: update dependencies
種類：
feat ：新機能（マイナーバージョンアップ）
fix ：バグ修正（PATCHバージョンのアップ）
docs : ドキュメントのみの変更
style : コードの意味に影響を与えない変更
refactor : バグを修正したり機能を追加したりしないコード変更
perf : パフォーマンスを向上させるコード変更
test : 不足しているテストを追加したり、既存のテストを修正したりする
chore : ビルドプロセスまたは補助ツールの変更
重大な変更: コミット フッターにBREAKING CHANGE:を追加して、メジャー バージョンをアップグレードします。
Node.js
タイプスクリプト
@modelcontextprotocol/sdk (バージョン: ^1.9.0)
ゾッド（バージョン: ^3.22.4）
マサチューセッツ工科大学
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.
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