Ck: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 .codex/.mcp.json.

Gemini CLI Integration (recommended): Create symlink to .gemini/settings.json:

bashCopy code
mkdir -p .gemini && ln -sf .codex/.mcp.json .gemini/settings.json

See references/configuration.md and references/gemini-cli-integration.md.

GEMINI.md Response Format: Project root contains GEMINI.md that Gemini CLI auto-loads, enforcing structured JSON responses:

jsonCopy code
{"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

bashCopy code
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)

bashCopy code
# 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

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

Quick Example:

bashCopy code
# 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:

jsonCopy code
{"server":"puppeteer","tool":"screenshot","success":true,"result":"screenshot.png","error":null}

Benefits:

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

See 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 .codex/.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)

bashCopy code
npm install -g gemini-cli
mkdir -p .gemini && ln -sf .codex/.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":null}

Method 2: Scripts

bashCopy code
cd .agents/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

1. Gemini CLI (Primary): Fast, automatic, intelligent tool selection

   • Check: command -v gemini
   • Execute: echo "<task>" | gemini -y -m <gemini.model>
   • IMPORTANT: Use stdin piping, NOT -p flag (deprecated, skips MCP init)
   • 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.