Install any skill in seconds. Free to start, no credit card required.
Get Started Free →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.
.claude/skills/microck-mcp-management/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 13% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 54% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 42% | 0% |
Skill for managing and interacting with Model Context Protocol (MCP) servers.
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:
Use this skill when:
MCP servers configured in .claude/.mcp.json.
Gemini CLI Integration (recommended): Create symlink to .gemini/settings.json:
bashmkdir -p .gemini && ln -sf .claude/.mcp.json .gemini/settings.json
See references/configuration.md and references/gemini-cli-integration.md.
bashnpx 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.
LLM analyzes assets/tools.json directly - better than keyword matching algorithms.
Primary: Gemini CLI (if available)
bashgemini -y -m gemini-2.5-flash -p "Take a screenshot of https://example.com"
Secondary: Direct Scripts
bashnpx tsx scripts/cli.ts call-tool memory create_entities '{"entities":[...]}'
Fallback: mcp-manager Subagent
See references/gemini-cli-integration.md for complete examples.
Use Gemini CLI for automatic tool discovery and execution. See references/gemini-cli-integration.md for complete guide.
Quick Example:
bashgemini -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.
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.
LLM reads assets/tools.json, intelligently selects relevant tools using context understanding, synonyms, and intent recognition.
Coordinate tools across multiple servers. Each tool knows its source server for proper routing.
Core MCP client manager class. Handles:
.claude/.mcp.jsonCommand-line interface for MCP operations. Commands:
list-tools - Display all tools and save to assets/tools.jsonlist-prompts - Display all promptslist-resources - Display all resourcescall-tool <server> <tool> <json> - Execute a toolNote: list-tools persists complete tool catalog to assets/tools.json with full schemas for fast reference, offline browsing, and version control.
Method 1: Gemini CLI (recommended)
bashnpm 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
bashcd .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.
See references/mcp-protocol.md for:
command -v geminigemini -y -m gemini-2.5-flash -p "<task>"npx tsx scripts/cli.ts call-tool <server> <tool> <args>The mcp-manager agent uses this skill to:
gemini command if availableThis keeps main agent context clean and enables efficient MCP integration.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 17,262 | 4,512 | -74% | 1 | 1 | 0% | 3,358 | 1,734 | -48% | 0 | 0 | — |
case-02 | fail→fail | 7,975 | 9,251 | +16% | 1 | 1 | 0% | 1,233 | 1,925 | +56% | 0 | 0 | — |
case-03 | fail→fail | 4,911 | 4,667 | -5% | 1 | 1 | 0% | 920 | 1,749 | +90% | 0 | 0 | — |
case-04 | fail→pass | 10,251 | 3,319 | -68% | 1 | 1 | 0% | 1,812 | 2,044 | +13% | 0 | 0 | — |
case-05 | fail→pass | 8,336 | 3,219 | -61% | 1 | 1 | 0% | 1,296 | 1,991 | +54% | 0 | 0 | — |
case-06 | fail→pass | 13,494 | 6,947 | -49% | 1 | 1 | 0% | 2,130 | 2,674 | +26% | 0 | 0 | — |
case-07 | fail→pass | 9,167 | 2,363 | -74% | 1 | 1 | 0% | 1,509 | 1,856 | +23% | 0 | 0 | — |
case-08 | fail→pass | 8,012 | 2,592 | -68% | 1 | 1 | 0% | 1,329 | 1,892 | +42% | 0 | 0 | — |
case-09 | fail→pass | 5,827 | 2,168 | -63% | 1 | 1 | 0% | 971 | 1,765 | +82% | 0 | 0 | — |
case-10 | pass→pass | 11,129 | 10,638 | -4% | 1 | 1 | 0% | 1,710 | 3,210 | +88% | 0 | 0 | — |
case-11 | fail→pass | 5,764 | 2,313 | -60% | 1 | 1 | 0% | 885 | 1,859 | +110% | 0 | 0 | — |
case-12 | pass→pass | 13,389 | 11,659 | -13% | 1 | 1 | 0% | 2,052 | 3,304 | +61% | 0 | 0 | — |
case-13 | pass→pass | 13,855 | 6,742 | -51% | 1 | 1 | 0% | 2,347 | 2,587 | +10% | 0 | 0 | — |
case-14 | pass→pass | 7,458 | 3,546 | -52% | 1 | 1 | 0% | 1,356 | 2,112 | +56% | 0 | 0 | — |
case-15 | fail→pass | 25,169 | 2,960 | -88% | 1 | 1 | 0% | 1,057 | 1,851 | +75% | 0 | 0 | — |
case-16 | pass→pass | 12,880 | 1,992 | -85% | 1 | 1 | 0% | 1,130 | 1,785 | +58% | 0 | 0 | — |
case-17 | pass→pass | 8,634 | 1,892 | -78% | 1 | 1 | 0% | 1,345 | 1,769 | +32% | 0 | 0 | — |
case-18 | pass→pass | 9,156 | 6,922 | -24% | 1 | 1 | 0% | 1,542 | 2,576 | +67% | 0 | 0 | — |
case-19 | pass→pass | 5,047 | 2,734 | -46% | 1 | 1 | 0% | 780 | 1,916 | +146% | 0 | 0 | — |
case-20 | pass→pass | 9,651 | 8,478 | -12% | 1 | 1 | 0% | 1,871 | 3,078 | +65% | 0 | 0 | — |
case-21 | pass→pass | 10,223 | 8,941 | -13% | 1 | 1 | 0% | 2,125 | 3,310 | +56% | 0 | 0 | — |
case-22 | pass→pass | 5,048 | 2,563 | -49% | 1 | 1 | 0% | 818 | 1,944 | +138% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 18 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +36 percentage points is the difference between those two pass rates over the 18 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.