mcp-management

name	mcp-management
version	1.0.0
description	[AI & Tools] Use when discovering, filtering, executing, or integrating MCP tools, prompts, and resources.
disable-model-invocation	true

Quick Summary

Goal: Discover, analyze, and execute MCP tools/prompts/resources from configured servers without polluting main context.

Workflow:

1. Config Management — Use .claude/.mcp.json, symlink to .gemini/settings.json for Gemini CLI
2. Capability Discovery — npx tsx scripts/cli.ts list-tools saves to assets/tools.json
3. Intelligent Selection — LLM analyzes tools.json for task-relevant capabilities
4. Execution — Primary: Gemini CLI with stdin piping; Secondary: Direct scripts; Fallback: general-purpose subagent

Key Rules:

• Gemini CLI Primary: Use stdin piping (echo "task" | gemini), NOT -p flag (skips MCP init)
• GEMINI.md Auto-Load: Project root file enforces structured JSON responses from Gemini
• Progressive Disclosure: Load only needed capabilities, subagents handle discovery
• Persistent Catalog: list-tools saves complete schemas to assets/tools.json for fast reference

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

MCP Management

Skill for managing and interacting with Model Context Protocol (MCP) servers.

Prerequisites

⚠️ MUST ATTENTION READ references/configuration.md and references/gemini-cli-integration.md before executing — contain MCP server configuration format, Gemini CLI setup, execution patterns, and troubleshooting required by Core Capabilities and Implementation Patterns sections below. For protocol internals, also ⚠️ MUST ATTENTION READ references/mcp-protocol.md.

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:

mkdir -p .gemini && ln -sf .claude/.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:

{"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

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)

# IMPORTANT: Use stdin piping, NOT -p flag (deprecated, skips MCP init)
echo "Take a screenshot of https://example.com" | gemini -y -m gemini-2.5-flash

Secondary: Direct Scripts

npx tsx scripts/cli.ts call-tool memory create_entities '{"entities":[...]}'

Fallback: General-Purpose 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:

# 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-2.5-flash

Expected Output:

{ "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 for complete guide.

Pattern 2: Subagent-Based Execution (Fallback)

Use general-purpose 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)

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-2.5-flash

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

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: General-Purpose 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-2.5-flash
   • 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. General-Purpose Subagent (Fallback): Context-efficient delegation

   • Use when: Gemini unavailable or failed
   • Keeps main context clean

Integration with Agents

The general-purpose 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.

Related

• mcp-builder
• claude-code

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[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

AI Mistake Prevention — Failure modes to avoid on every task: Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

Closing Reminders

IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act) IMPORTANT MUST ATTENTION add a final review todo task to verify work quality MANDATORY IMPORTANT MUST ATTENTION READ the following files before starting: IMPORTANT MUST ATTENTION READ references/configuration.md before starting IMPORTANT MUST ATTENTION READ references/mcp-protocol.md before starting

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.