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# MCP Management Skill
Intelligent management and execution of Model Context Protocol (MCP) servers.
## Overview
This skill enables Claude to discover, analyze, and execute MCP server capabilities without polluting the main context window. Perfect for context-efficient MCP integration using subagent-based architecture.
## Features
- **Multi-Server Management**: Connect to multiple MCP servers from single config
- **Intelligent Tool Discovery**: Analyze which tools are relevant for specific tasks
- **Progressive Disclosure**: Load only necessary tool definitions
- **Execution Engine**: Call MCP tools with proper parameter handling
- **Context Efficiency**: Delegate MCP operations to `mcp-manager` subagent
## Quick Start
### 1. Install Dependencies
```bash
cd .claude/skills/mcp-management/scripts
npm install
```
### 2. Configure MCP Servers
Create `.claude/.mcp.json`:
```json
{
"mcpServers": {
"memory": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-memory"]
},
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/allowed/path"]
}
}
}
```
See `.claude/.mcp.json.example` for more examples.
### 3. Test Connection
```bash
cd .claude/skills/mcp-management/scripts
npx ts-node cli.ts list-tools
```
## Usage Patterns
### Pattern 1: Discover Available Tools
```bash
npx ts-node scripts/cli.ts list-tools
npx ts-node scripts/cli.ts list-prompts
npx ts-node scripts/cli.ts list-resources
```
### Pattern 2: LLM-Driven Tool Selection
The LLM reads `assets/tools.json` and intelligently selects tools. No separate analysis command needed - the LLM's understanding of context and intent is superior to keyword matching.
### Pattern 3: Execute MCP Tools
```bash
npx ts-node scripts/cli.ts call-tool memory add '{"key":"name","value":"Alice"}'
```
### Pattern 4: Use with Subagent
In main Claude conversation:
```
User: "I need to search the web and save results"
Main Agent: [Spawns mcp-manager subagent]
mcp-manager: Discovers brave-search + memory tools, reports back
Main Agent: Uses recommended tools for implementation
```
## Architecture
```
Main Agent (Claude)
↓ (delegates MCP tasks)
mcp-manager Subagent
↓ (uses skill)
mcp-management Skill
↓ (connects via)
MCP Servers (memory, filesystem, etc.)
```
**Benefits**:
- Main agent context stays clean
- MCP discovery happens in isolated subagent context
- Only relevant tool definitions loaded when needed
- Reduced token usage
## File Structure
```
mcp-management/
├── SKILL.md # Skill definition
├── README.md # This file
├── scripts/
│ ├── mcp-client.ts # Core MCP client manager
│ ├── analyze-tools.ts # Intelligent tool selection
│ ├── cli.ts # Command-line interface
│ ├── package.json # Dependencies
│ ├── tsconfig.json # TypeScript config
│ └── .env.example # Environment template
└── references/
├── mcp-protocol.md # MCP protocol reference
└── configuration.md # Config guide
```
## Scripts Reference
### mcp-client.ts
Core client manager class:
- Load config from `.claude/.mcp.json`
- Connect to multiple MCP servers
- List/execute tools, prompts, resources
- Lifecycle management
### cli.ts
Command-line interface:
- `list-tools` - Show all tools and save to assets/tools.json
- `list-prompts` - Show all prompts
- `list-resources` - Show all resources
- `call-tool <server> <tool> <json>` - Execute tool
**Note**: Tool analysis is performed by the LLM reading `assets/tools.json`, which provides better context understanding than algorithmic matching.
## Configuration
### Environment Variables
Scripts check for variables in this order:
1. `process.env` (runtime)
2. `.claude/skills/mcp-management/.env`
3. `.claude/skills/.env`
4. `.claude/.env`
### MCP Config Format
```json
{
"mcpServers": {
"server-name": {
"command": "executable", // Required
"args": ["arg1", "arg2"], // Required
"env": { // Optional
"VAR": "value",
"API_KEY": "${ENV_VAR}" // Reference env vars
}
}
}
}
```
## Common MCP Servers
Install with `npx`:
- `@modelcontextprotocol/server-memory` - Key-value storage
- `@modelcontextprotocol/server-filesystem` - File operations
- `@modelcontextprotocol/server-brave-search` - Web search
- `@modelcontextprotocol/server-puppeteer` - Browser automation
- `@modelcontextprotocol/server-fetch` - HTTP requests
## Integration with mcp-manager Agent
The `mcp-manager` agent (`.claude/agents/mcp-manager.md`) uses this skill to:
1. **Discover**: Connect to MCP servers, list capabilities
2. **Analyze**: Filter relevant tools for tasks
3. **Execute**: Call MCP tools on behalf of main agent
4. **Report**: Send concise results back to main agent
This architecture keeps main context clean and enables efficient MCP integration.
## Troubleshooting
### "Config not found"
Ensure `.claude/.mcp.json` exists and is valid JSON.
### "Server connection failed"
Check:
- Server command is installed (`npx` packages installed?)
- Server args are correct
- Environment variables are set
### "Tool not found"
List available tools first:
```bash
npx ts-node scripts/cli.ts list-tools
```
## Resources
- [MCP Specification](https://modelcontextprotocol.io/specification/latest)
- [MCP TypeScript SDK](https://github.com/modelcontextprotocol/typescript-sdk)
- [Official MCP Servers](https://github.com/modelcontextprotocol/servers)
- [Skill References](./references/)
## License
MIT

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---
name: mcp-management
description: Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.
---
# MCP Management
Skill for managing and interacting with Model Context Protocol (MCP) servers.
## Overview
MCP is an open protocol enabling AI agents to connect to external tools and data sources. This skill provides scripts and utilities to discover, analyze, and execute MCP capabilities from configured servers without polluting the main context window.
**Key Benefits**:
- Progressive disclosure of MCP capabilities (load only what's needed)
- Intelligent tool/prompt/resource selection based on task requirements
- Multi-server management from single config file
- Context-efficient: subagents handle MCP discovery and execution
- Persistent tool catalog: automatically saves discovered tools to JSON for fast reference
## When to Use This Skill
Use this skill when:
1. **Discovering MCP Capabilities**: Need to list available tools/prompts/resources from configured servers
2. **Task-Based Tool Selection**: Analyzing which MCP tools are relevant for a specific task
3. **Executing MCP Tools**: Calling MCP tools programmatically with proper parameter handling
4. **MCP Integration**: Building or debugging MCP client implementations
5. **Context Management**: Avoiding context pollution by delegating MCP operations to subagents
## Core Capabilities
### 1. Configuration Management
MCP servers configured in `.claude/.mcp.json`.
**Gemini CLI Integration** (recommended): Create symlink to `.gemini/settings.json`:
```bash
mkdir -p .gemini && ln -sf .claude/.mcp.json .gemini/settings.json
```
See [references/configuration.md](references/configuration.md) and [references/gemini-cli-integration.md](references/gemini-cli-integration.md).
### 2. Capability Discovery
```bash
npx tsx scripts/cli.ts list-tools # Saves to assets/tools.json
npx tsx scripts/cli.ts list-prompts
npx tsx scripts/cli.ts list-resources
```
Aggregates capabilities from multiple servers with server identification.
### 3. Intelligent Tool Analysis
LLM analyzes `assets/tools.json` directly - better than keyword matching algorithms.
### 4. Tool Execution
**Primary: Gemini CLI** (if available)
```bash
gemini -y -m gemini-2.5-flash -p "Take a screenshot of https://example.com"
```
**Secondary: Direct Scripts**
```bash
npx tsx scripts/cli.ts call-tool memory create_entities '{"entities":[...]}'
```
**Fallback: mcp-manager Subagent**
See [references/gemini-cli-integration.md](references/gemini-cli-integration.md) for complete examples.
## Implementation Patterns
### Pattern 1: Gemini CLI Auto-Execution (Primary)
Use Gemini CLI for automatic tool discovery and execution. See [references/gemini-cli-integration.md](references/gemini-cli-integration.md) for complete guide.
**Quick Example**:
```bash
gemini -y -m gemini-2.5-flash -p "Take a screenshot of https://example.com"
```
**Benefits**: Automatic tool discovery, natural language execution, faster than subagent orchestration.
### Pattern 2: Subagent-Based Execution (Fallback)
Use `mcp-manager` agent when Gemini CLI unavailable. Subagent discovers tools, selects relevant ones, executes tasks, reports back.
**Benefit**: Main context stays clean, only relevant tool definitions loaded when needed.
### Pattern 3: LLM-Driven Tool Selection
LLM reads `assets/tools.json`, intelligently selects relevant tools using context understanding, synonyms, and intent recognition.
### Pattern 4: Multi-Server Orchestration
Coordinate tools across multiple servers. Each tool knows its source server for proper routing.
## Scripts Reference
### scripts/mcp-client.ts
Core MCP client manager class. Handles:
- Config loading from `.claude/.mcp.json`
- Connecting to multiple MCP servers
- Listing tools/prompts/resources across all servers
- Executing tools with proper error handling
- Connection lifecycle management
### scripts/cli.ts
Command-line interface for MCP operations. Commands:
- `list-tools` - Display all tools and save to `assets/tools.json`
- `list-prompts` - Display all prompts
- `list-resources` - Display all resources
- `call-tool <server> <tool> <json>` - Execute a tool
**Note**: `list-tools` persists complete tool catalog to `assets/tools.json` with full schemas for fast reference, offline browsing, and version control.
## Quick Start
**Method 1: Gemini CLI** (recommended)
```bash
npm install -g gemini-cli
mkdir -p .gemini && ln -sf .claude/.mcp.json .gemini/settings.json
gemini -y -m gemini-2.5-flash -p "Take a screenshot of https://example.com"
```
**Method 2: Scripts**
```bash
cd .claude/skills/mcp-management/scripts && npm install
npx tsx cli.ts list-tools # Saves to assets/tools.json
npx tsx cli.ts call-tool memory create_entities '{"entities":[...]}'
```
**Method 3: mcp-manager Subagent**
See [references/gemini-cli-integration.md](references/gemini-cli-integration.md) for complete guide.
## Technical Details
See [references/mcp-protocol.md](references/mcp-protocol.md) for:
- JSON-RPC protocol details
- Message types and formats
- Error codes and handling
- Transport mechanisms (stdio, HTTP+SSE)
- Best practices
## Integration Strategy
### Execution Priority
1. **Gemini CLI** (Primary): Fast, automatic, intelligent tool selection
- Check: `command -v gemini`
- Execute: `gemini -y -m gemini-2.5-flash -p "<task>"`
- Best for: All tasks when available
2. **Direct CLI Scripts** (Secondary): Manual tool specification
- Use when: Need specific tool/server control
- Execute: `npx tsx scripts/cli.ts call-tool <server> <tool> <args>`
3. **mcp-manager Subagent** (Fallback): Context-efficient delegation
- Use when: Gemini unavailable or failed
- Keeps main context clean
### Integration with Agents
The `mcp-manager` agent uses this skill to:
- Check Gemini CLI availability first
- Execute via `gemini` command if available
- Fallback to direct script execution
- Discover MCP capabilities without loading into main context
- Report results back to main agent
This keeps main agent context clean and enables efficient MCP integration.

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# MCP Configuration Guide
## Configuration File Structure
MCP servers are configured in `.claude/.mcp.json`:
```json
{
"mcpServers": {
"server-name": {
"command": "executable",
"args": ["arg1", "arg2"],
"env": {
"API_KEY": "value"
}
}
}
}
```
## Common Server Configurations
### Memory Server
Store and retrieve key-value data:
```json
{
"memory": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-memory"]
}
}
```
### Filesystem Server
File operations with restricted access:
```json
{
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/allowed/path"
]
}
}
```
### Brave Search Server
Web search capabilities:
```json
{
"brave-search": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-brave-search"],
"env": {
"BRAVE_API_KEY": "${BRAVE_API_KEY}"
}
}
}
```
### Puppeteer Server
Browser automation:
```json
{
"puppeteer": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-puppeteer"]
}
}
```
## Environment Variables
Reference env vars with `${VAR_NAME}` syntax:
```json
{
"api-server": {
"command": "node",
"args": ["server.js"],
"env": {
"API_KEY": "${MY_API_KEY}",
"BASE_URL": "${API_BASE_URL}"
}
}
}
```
## Configuration Loading Order
Scripts check for config in this order:
1. `process.env` (runtime environment)
2. `.claude/skills/mcp-management/.env`
3. `.claude/skills/.env`
4. `.claude/.env`
## Validation
Config must:
- Be valid JSON
- Include `mcpServers` object
- Each server must have `command` and `args`
- `env` is optional but must be object if present

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# Gemini CLI Integration Guide
## Overview
Gemini CLI provides automatic MCP tool discovery and execution via natural language prompts. This is the recommended primary method for executing MCP tools.
## Installation
```bash
npm install -g gemini-cli
```
Verify installation:
```bash
gemini --version
```
## Configuration
### Symlink Setup
Gemini CLI reads MCP servers from `.gemini/settings.json`. Create a symlink to `.claude/.mcp.json`:
```bash
# Create .gemini directory
mkdir -p .gemini
# Create symlink (Unix/Linux/macOS)
ln -sf .claude/.mcp.json .gemini/settings.json
# Create symlink (Windows - requires admin or developer mode)
mklink .gemini\settings.json .claude\.mcp.json
```
### Security
Add to `.gitignore`:
```
.gemini/settings.json
```
This prevents committing sensitive API keys and server configurations.
## Usage
### Basic Syntax
```bash
gemini [flags] -p "<prompt>"
```
### Essential Flags
- `-y`: Skip confirmation prompts (auto-approve tool execution)
- `-m <model>`: Model selection
- `gemini-2.5-flash` (fast, recommended for MCP)
- `gemini-2.5-flash` (balanced)
- `gemini-pro` (high quality)
- `-p "<prompt>"`: Task description
### Examples
**Screenshot Capture**:
```bash
gemini -y -m gemini-2.5-flash -p "Take a screenshot of https://www.google.com.vn"
```
**Memory Operations**:
```bash
gemini -y -m gemini-2.5-flash -p "Remember that Alice is a React developer working on e-commerce projects"
```
**Web Research**:
```bash
gemini -y -m gemini-2.5-flash -p "Search for latest Next.js 15 features and summarize the top 3"
```
**Multi-Tool Orchestration**:
```bash
gemini -y -m gemini-2.5-flash -p "Search for Claude AI documentation, take a screenshot of the homepage, and save both to memory"
```
**Browser Automation**:
```bash
gemini -y -m gemini-2.5-flash -p "Navigate to https://example.com, click the signup button, and take a screenshot"
```
## How It Works
1. **Configuration Loading**: Reads `.gemini/settings.json` (symlinked to `.claude/.mcp.json`)
2. **Server Connection**: Connects to all configured MCP servers
3. **Tool Discovery**: Lists all available tools from servers
4. **Prompt Analysis**: Gemini model analyzes the prompt
5. **Tool Selection**: Automatically selects relevant tools
6. **Execution**: Calls tools with appropriate parameters
7. **Result Synthesis**: Combines tool outputs into coherent response
## Advanced Configuration
### Trusted Servers (Skip Confirmations)
Edit `.claude/.mcp.json`:
```json
{
"mcpServers": {
"memory": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-memory"],
"trust": true
}
}
}
```
With `trust: true`, the `-y` flag is unnecessary.
### Tool Filtering
Limit tool exposure:
```json
{
"mcpServers": {
"chrome-devtools": {
"command": "npx",
"args": ["-y", "chrome-devtools-mcp@latest"],
"includeTools": ["navigate_page", "screenshot"],
"excludeTools": ["evaluate_js"]
}
}
}
```
### Environment Variables
Use `$VAR_NAME` syntax for sensitive data:
```json
{
"mcpServers": {
"brave-search": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-brave-search"],
"env": {
"BRAVE_API_KEY": "$BRAVE_API_KEY"
}
}
}
}
```
## Troubleshooting
### Check MCP Status
```bash
gemini
> /mcp
```
Shows:
- Connected servers
- Available tools
- Configuration errors
### Verify Symlink
```bash
# Unix/Linux/macOS
ls -la .gemini/settings.json
# Windows
dir .gemini\settings.json
```
Should show symlink pointing to `.claude/.mcp.json`.
### Debug Mode
```bash
gemini --debug -p "Take a screenshot"
```
Shows detailed MCP communication logs.
## Comparison with Alternatives
| Method | Speed | Flexibility | Setup | Best For |
|--------|-------|-------------|-------|----------|
| Gemini CLI | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ | All tasks |
| Direct Scripts | ⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | Specific tools |
| mcp-manager | ⭐ | ⭐⭐ | ⭐⭐⭐ | Fallback |
**Recommendation**: Use Gemini CLI as primary method, fallback to scripts/subagent when unavailable.
## Resources
- [Gemini CLI Documentation](https://geminicli.com/docs)
- [MCP Server Configuration](https://geminicli.com/docs/tools/mcp-server)
- [Tool Reference](https://geminicli.com/docs/tools/mcp-server/#tool-interaction)

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# Model Context Protocol (MCP) Reference
## Protocol Overview
MCP is JSON-RPC 2.0 based protocol for AI-tool integration.
**Version**: 2025-03-26
**Foundation**: JSON-RPC 2.0
**Architecture**: Client-Host-Server
## Connection Lifecycle
1. **Initialize**: Client sends `initialize` request with capabilities
2. **Response**: Server responds with its capabilities
3. **Handshake**: Client sends `notifications/initialized`
4. **Active**: Bidirectional messaging
5. **Shutdown**: Close connections, cleanup
## Core Capabilities
### Tools (Executable Functions)
Tools are functions that servers expose for execution.
**List Tools**:
```json
{"method": "tools/list"}
```
**Call Tool**:
```json
{
"method": "tools/call",
"params": {
"name": "tool_name",
"arguments": {}
}
}
```
### Prompts (Interaction Templates)
Prompts are reusable templates for LLM interactions.
**List Prompts**:
```json
{"method": "prompts/list"}
```
**Get Prompt**:
```json
{
"method": "prompts/get",
"params": {
"name": "prompt_name",
"arguments": {}
}
}
```
### Resources (Data Sources)
Resources expose read-only data to clients.
**List Resources**:
```json
{"method": "resources/list"}
```
**Read Resource**:
```json
{
"method": "resources/read",
"params": {"uri": "resource://path"}
}
```
## Transport Types
### stdio (Local)
Server runs as subprocess. Messages via stdin/stdout.
```typescript
const transport = new StdioClientTransport({
command: 'node',
args: ['server.js']
});
```
### HTTP+SSE (Remote)
POST for requests, GET for server events.
```typescript
const transport = new StreamableHTTPClientTransport({
url: 'http://localhost:3000/mcp'
});
```
## Error Codes
- **-32700**: Parse error
- **-32600**: Invalid request
- **-32601**: Method not found
- **-32602**: Invalid params
- **-32603**: Internal error
- **-32002**: Resource not found (MCP-specific)
## Best Practices
1. **Progressive Disclosure**: Load tool definitions on-demand
2. **Context Efficiency**: Filter data before returning
3. **Security**: Validate inputs, sanitize outputs
4. **Resource Management**: Cleanup connections properly
5. **Error Handling**: Handle all error cases gracefully

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# MCP Management Scripts Environment Variables
# Path to MCP configuration file (optional, defaults to .claude/.mcp.json)
MCP_CONFIG_PATH=.claude/.mcp.json
# Logging level (optional, defaults to info)
LOG_LEVEL=info
# Enable debug mode (optional, defaults to false)
DEBUG=false

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#!/usr/bin/env node
/**
* MCP Management CLI - Command-line interface for MCP operations
*/
import { MCPClientManager } from './mcp-client.js';
import { writeFileSync, mkdirSync } from 'fs';
import { dirname, join } from 'path';
import { fileURLToPath } from 'url';
const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
async function main() {
const args = process.argv.slice(2);
const command = args[0];
const manager = new MCPClientManager();
try {
// Load config
await manager.loadConfig();
console.log('✓ Config loaded');
// Connect to all servers
await manager.connectAll();
console.log('✓ Connected to all MCP servers\n');
switch (command) {
case 'list-tools':
await listTools(manager);
break;
case 'list-prompts':
await listPrompts(manager);
break;
case 'list-resources':
await listResources(manager);
break;
case 'call-tool':
await callTool(manager, args[1], args[2], args[3]);
break;
default:
printUsage();
}
await manager.cleanup();
} catch (error) {
console.error('Error:', error);
process.exit(1);
}
}
async function listTools(manager: MCPClientManager) {
const tools = await manager.getAllTools();
console.log(`Found ${tools.length} tools:\n`);
for (const tool of tools) {
console.log(`📦 ${tool.serverName} / ${tool.name}`);
console.log(` ${tool.description}`);
if (tool.inputSchema?.properties) {
console.log(` Parameters: ${Object.keys(tool.inputSchema.properties).join(', ')}`);
}
console.log('');
}
// Save tools to JSON file
const assetsDir = join(__dirname, '..', 'assets');
const toolsPath = join(assetsDir, 'tools.json');
try {
mkdirSync(assetsDir, { recursive: true });
writeFileSync(toolsPath, JSON.stringify(tools, null, 2));
console.log(`\n✓ Tools saved to ${toolsPath}`);
} catch (error) {
console.error(`\n✗ Failed to save tools: ${error}`);
}
}
async function listPrompts(manager: MCPClientManager) {
const prompts = await manager.getAllPrompts();
console.log(`Found ${prompts.length} prompts:\n`);
for (const prompt of prompts) {
console.log(`💬 ${prompt.serverName} / ${prompt.name}`);
console.log(` ${prompt.description}`);
if (prompt.arguments && prompt.arguments.length > 0) {
console.log(` Arguments: ${prompt.arguments.map((a: any) => a.name).join(', ')}`);
}
console.log('');
}
}
async function listResources(manager: MCPClientManager) {
const resources = await manager.getAllResources();
console.log(`Found ${resources.length} resources:\n`);
for (const resource of resources) {
console.log(`📄 ${resource.serverName} / ${resource.name}`);
console.log(` URI: ${resource.uri}`);
if (resource.description) {
console.log(` ${resource.description}`);
}
if (resource.mimeType) {
console.log(` Type: ${resource.mimeType}`);
}
console.log('');
}
}
async function callTool(
manager: MCPClientManager,
serverName: string,
toolName: string,
argsJson: string
) {
if (!serverName || !toolName || !argsJson) {
console.error('Usage: cli.ts call-tool <server> <tool> <json-args>');
process.exit(1);
}
const args = JSON.parse(argsJson);
console.log(`Calling ${serverName}/${toolName}...`);
const result = await manager.callTool(serverName, toolName, args);
console.log('\nResult:');
console.log(JSON.stringify(result, null, 2));
}
function printUsage() {
console.log(`
MCP Management CLI
Usage:
cli.ts <command> [options]
Commands:
list-tools List all tools and save to assets/tools.json
list-prompts List all prompts from all MCP servers
list-resources List all resources from all MCP servers
call-tool <server> <tool> <json> Call a specific tool
Examples:
cli.ts list-tools
cli.ts call-tool memory create_entities '{"entities":[{"name":"Alice","entityType":"person"}]}'
cli.ts call-tool human-mcp playwright_screenshot_fullpage '{"url":"https://example.com"}'
Note: Tool analysis is done by the LLM reading assets/tools.json directly.
`);
}
main();

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skills/mcp-management/scripts/mcp-client.ts Normal file
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#!/usr/bin/env node
/**
* MCP Client - Core client for interacting with MCP servers
*/
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js';
import { readFile } from 'fs/promises';
import { resolve } from 'path';
interface MCPConfig {
mcpServers: {
[key: string]: {
command: string;
args: string[];
env?: Record<string, string>;
};
};
}
interface ToolInfo {
serverName: string;
name: string;
description: string;
inputSchema: any;
outputSchema?: any;
}
interface PromptInfo {
serverName: string;
name: string;
description: string;
arguments?: any[];
}
interface ResourceInfo {
serverName: string;
uri: string;
name: string;
description?: string;
mimeType?: string;
}
export class MCPClientManager {
private config: MCPConfig | null = null;
private clients: Map<string, Client> = new Map();
async loadConfig(configPath: string = '.claude/.mcp.json'): Promise<MCPConfig> {
const fullPath = resolve(process.cwd(), configPath);
const content = await readFile(fullPath, 'utf-8');
const config = JSON.parse(content) as MCPConfig;
this.config = config;
return config;
}
async connectToServer(serverName: string): Promise<Client> {
if (!this.config?.mcpServers[serverName]) {
throw new Error(`Server ${serverName} not found in config`);
}
const serverConfig = this.config.mcpServers[serverName];
const transport = new StdioClientTransport({
command: serverConfig.command,
args: serverConfig.args,
env: serverConfig.env
});
const client = new Client({
name: `mcp-manager-${serverName}`,
version: '1.0.0'
}, { capabilities: {} });
await client.connect(transport);
this.clients.set(serverName, client);
return client;
}
async connectAll(): Promise<void> {
if (!this.config) {
throw new Error('Config not loaded. Call loadConfig() first.');
}
const connections = Object.keys(this.config.mcpServers).map(name =>
this.connectToServer(name)
);
await Promise.all(connections);
}
async getAllTools(): Promise<ToolInfo[]> {
const allTools: ToolInfo[] = [];
for (const [serverName, client] of this.clients.entries()) {
const response = await client.listTools({}, { timeout: 300000 });
for (const tool of response.tools) {
allTools.push({
serverName,
name: tool.name,
description: tool.description || '',
inputSchema: tool.inputSchema,
outputSchema: (tool as any).outputSchema
});
}
}
return allTools;
}
async getAllPrompts(): Promise<PromptInfo[]> {
const allPrompts: PromptInfo[] = [];
for (const [serverName, client] of this.clients.entries()) {
const response = await client.listPrompts({}, { timeout: 300000 });
for (const prompt of response.prompts) {
allPrompts.push({
serverName,
name: prompt.name,
description: prompt.description || '',
arguments: prompt.arguments
});
}
}
return allPrompts;
}
async getAllResources(): Promise<ResourceInfo[]> {
const allResources: ResourceInfo[] = [];
for (const [serverName, client] of this.clients.entries()) {
const response = await client.listResources({}, { timeout: 300000 });
for (const resource of response.resources) {
allResources.push({
serverName,
uri: resource.uri,
name: resource.name,
description: resource.description,
mimeType: resource.mimeType
});
}
}
return allResources;
}
async callTool(serverName: string, toolName: string, args: any): Promise<any> {
const client = this.clients.get(serverName);
if (!client) throw new Error(`Not connected to server: ${serverName}`);
return await client.callTool({ name: toolName, arguments: args }, { timeout: 300000 });
}
async getPrompt(serverName: string, promptName: string, args?: any): Promise<any> {
const client = this.clients.get(serverName);
if (!client) throw new Error(`Not connected to server: ${serverName}`);
return await client.getPrompt({ name: promptName, arguments: args }, { timeout: 300000 });
}
async readResource(serverName: string, uri: string): Promise<any> {
const client = this.clients.get(serverName);
if (!client) throw new Error(`Not connected to server: ${serverName}`);
return await client.readResource({ uri }, { timeout: 300000 });
}
async cleanup(): Promise<void> {
for (const client of this.clients.values()) {
await client.close();
}
this.clients.clear();
}
}

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skills/mcp-management/scripts/package.json Normal file
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{
"name": "mcp-management-scripts",
"version": "1.0.0",
"type": "module",
"description": "MCP client scripts for managing MCP servers",
"scripts": {
"build": "tsc",
"test": "node --loader ts-node/esm test.ts"
},
"dependencies": {
"@modelcontextprotocol/sdk": "^1.0.0"
},
"devDependencies": {
"@types/node": "^20.0.0",
"typescript": "^5.0.0",
"ts-node": "^10.0.0"
}
}

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skills/mcp-management/scripts/tsconfig.json Normal file
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{
"compilerOptions": {
"target": "ES2022",
"module": "ES2022",
"moduleResolution": "node",
"esModuleInterop": true,
"strict": true,
"skipLibCheck": true,
"outDir": "./dist",
"rootDir": "./",
"resolveJsonModule": true
},
"include": ["*.ts"],
"exclude": ["node_modules", "dist"]
}