mcp-management
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.
When & Why to Use This Skill
The MCP Management skill is a comprehensive framework designed to empower AI agents with the ability to discover, analyze, and execute capabilities from Model Context Protocol (MCP) servers. It optimizes agent performance by implementing progressive disclosure of tools and resources, ensuring that the main conversation context remains clean while providing access to a vast ecosystem of external integrations. By supporting multi-server management and intelligent tool selection, it serves as a critical infrastructure component for building scalable and context-efficient AI agent workflows.
Use Cases
- 1. Dynamic Capability Discovery: Automatically scan and catalog available tools, prompts, and resources from multiple configured MCP servers to expand agent functionality on the fly.
- 2. Context-Efficient Tool Execution: Delegate complex tool operations to subagents or the Gemini CLI to prevent large tool schemas from exhausting the primary model's context window.
- 3. Intelligent Tool Selection: Utilize LLM-driven analysis of persistent tool catalogs (assets/tools.json) to accurately match user intent with the most relevant MCP capabilities.
- 4. Multi-Server Orchestration: Centralize the management of diverse MCP integrations, allowing the agent to route tasks across different specialized servers like memory, databases, and web search engines from a single configuration.
| 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:
- Discovering MCP Capabilities: Need to list available tools/prompts/resources from configured servers
- Task-Based Tool Selection: Analyzing which MCP tools are relevant for a specific task
- Executing MCP Tools: Calling MCP tools programmatically with proper parameter handling
- MCP Integration: Building or debugging MCP client implementations
- 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:
mkdir -p .gemini && ln -sf .claude/.mcp.json .gemini/settings.json
See references/configuration.md and references/gemini-cli-integration.md.
2. Capability Discovery
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)
gemini -y -m gemini-2.5-flash -p "Take a screenshot of https://example.com"
Secondary: Direct Scripts
npx tsx scripts/cli.ts call-tool memory create_entities '{"entities":[...]}'
Fallback: mcp-manager Subagent
See 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 for complete guide.
Quick Example:
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 toassets/tools.jsonlist-prompts- Display all promptslist-resources- Display all resourcescall-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)
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
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 for complete guide.
Technical Details
See 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
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
- Check:
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>
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
geminicommand 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.