vc:mcp-management

# 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).

**GEMINI.md Response Format**: Project root contains `GEMINI.md` that Gemini CLI auto-loads, enforcing structured JSON responses:
```json
{"server":"name","tool":"name","success":true,"result":<data>,"error":null}
```

This ensures parseable, consistent output instead of unpredictable natural language. The file defines:
- Mandatory JSON-only response format (no markdown, no explanations)
- Maximum 500 character responses
- Error handling structure
- Available MCP servers reference

**Benefits**: Programmatically parseable output, consistent error reporting, DRY configuration (format defined once), context-efficient (auto-loaded by Gemini CLI).

### 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
# IMPORTANT: Use stdin piping, NOT -p flag (deprecated, skips MCP init)
echo "Take a screenshot of https://example.com" | gemini -y -m <gemini.model>
```

**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. Gemini CLI auto-loads `GEMINI.md` from project root to enforce structured JSON responses.

**Quick Example**:
```bash
# IMPORTANT: Use stdin piping, NOT -p flag (deprecated, skips MCP init)
# Add "Return JSON only per GEMINI.md instructions" to enforce structured output
echo "Take a screenshot of https://example.com. Return JSON only per GEMINI.md instructions." | gemini -y -m <gemini.model>
```

**Expected Output**:
```json
{"server":"puppeteer","tool":"screenshot","success":true,"result":"screenshot.png","error":null}
```

**Benefits**:
- Automatic tool discovery
- Structured JSON responses (parseable by Claude)
- GEMINI.md auto-loaded for consistent formatting
- Faster than subagent orchestration
- No natural language ambiguity

See [references/gemini-cli-integration.md](references/gemini-cli-integration.md) for complete guide.

### 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
# IMPORTANT: Use stdin piping, NOT -p flag (deprecated, skips MCP init)
# GEMINI.md auto-loads to enforce JSON responses
echo "Take a screenshot of https://example.com. Return JSON only per GEMINI.md instructions." | gemini -y -m <gemini.model>
```

Returns structured JSON: `{"server":"puppeteer","tool":"screenshot","success":true,"result":"screenshot.png","error"