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claude-prompts-mcp

MCP prompt template server: hot-reload, thinking frameworks, quality gates

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Updated
Jan 8, 2026
Validated
Jan 9, 2026

Claude Prompts MCP Server

Claude Prompts MCP Server Logo

npm version Add claude-prompts MCP server to Cursor License: MIT

Hot-reloadable prompts, structured reasoning, and chain workflows for your AI assistant.

Quick StartWhat You GetSyntaxDocs


Why

Stop copy-pasting prompts. This server turns your prompt library into a programmable engine:

  • Version Control — Prompts are YAML + templates in git. Track changes, review diffs.
  • Hot Reload — Edit a template, run it immediately. No restarts.
  • Structured Execution — Parses operators, injects methodology, enforces quality gates.

How It Works

%%{init: {'theme': 'neutral', 'themeVariables': {'background':'#0b1224','primaryColor':'#e2e8f0','primaryBorderColor':'#1f2937','primaryTextColor':'#0f172a','lineColor':'#94a3b8','fontFamily':'"DM Sans","Segoe UI",sans-serif','fontSize':'14px','edgeLabelBackground':'#0b1224'}}}%%
flowchart TB
    classDef actor fill:#0f172a,stroke:#cbd5e1,stroke-width:1.5px,color:#f8fafc;
    classDef server fill:#111827,stroke:#fbbf24,stroke-width:1.8px,color:#f8fafc;
    classDef process fill:#e2e8f0,stroke:#1f2937,stroke-width:1.6px,color:#0f172a;
    classDef client fill:#f4d0ff,stroke:#a855f7,stroke-width:1.6px,color:#2e1065;
    classDef clientbg fill:#1a0a24,stroke:#a855f7,stroke-width:1.8px,color:#f8fafc;
    classDef decision fill:#fef3c7,stroke:#f59e0b,stroke-width:1.6px,color:#78350f;

    linkStyle default stroke:#94a3b8,stroke-width:2px

    User["1. User sends command"]:::actor
    Example[">>analyze @CAGEERF :: 'cite sources'"]:::actor
    User --> Example --> Parse

    subgraph Server["MCP Server"]
        direction TB
        Parse["2. Parse operators"]:::process
        Inject["3. Inject framework + gates"]:::process
        Render["4. Render prompt"]:::process
        Decide{"6. Route verdict"}:::decision
        Parse --> Inject --> Render
    end
    Server:::server

    subgraph Client["Claude (Client)"]
        direction TB
        Execute["5. Run prompt + check gates"]:::client
    end
    Client:::clientbg

    Render -->|"Prompt with gate criteria"| Execute
    Execute -->|"Verdict + output"| Decide

    Decide -->|"PASS → render next step"| Render
    Decide -->|"FAIL → render retry prompt"| Render
    Decide -->|"Done"| Result["7. Return to user"]:::actor

The feedback loop: You send a command with operators → Server parses and injects methodology/gates → Claude executes and self-evaluates → Server routes: next step (PASS), retry (FAIL), or return result (done).


Quick Start

Claude Code (Recommended)

Step 1: Add the plugin marketplace (first time only)

/plugin marketplace add minipuft/minipuft-plugins

Step 2: Install the plugin

/plugin install claude-prompts@minipuft

Step 3: Try it

>>tech_evaluation_chain library:'zod' context:'API validation'
Why hooks matter

The plugin adds hooks that fix common issues:

ProblemHook Fix
Model ignores >>analyzeDetects syntax, suggests correct MCP call
Chain step forgottenInjects [Chain] Step 2/5 - continue
Gate review skippedReminds GATE_REVIEW: PASS|FAIL

Raw MCP works, but models sometimes miss the syntax. The hooks catch that. → hooks/README.md

User Data: Custom prompts stored in ~/.local/share/claude-prompts/ persist across updates.

Gemini CLI

# Install directly from GitHub
gemini extensions install https://github.com/minipuft/claude-prompts-mcp

# Development Setup (Hot Reload)
# Use a symbolic link to point the extension directory directly to your source code.
# This ensures changes to hooks and prompts are reflected immediately.
rm -rf ~/.gemini/extensions/gemini-prompts
ln -s "$(pwd)" ~/.gemini/extensions/gemini-prompts

The extension provides:

  • MCP server with the same tools (prompt_engine, resource_manager, system_control)
  • GEMINI.md context file with usage documentation

Optional hooks for >>prompt syntax detection can be enabled manually - see GEMINI.md for setup instructions.

Works with the same prompts, gates, and methodologies as Claude Code.

Claude Desktop

MethodInstall TimeUpdatesCustom Prompts
Desktop Extension10 secondsManualBuilt-in config
NPX30 secondsAutomaticVia env vars

Desktop Extension (one-click):

1. Download claude-prompts.mcpb → github.com/minipuft/claude-prompts-mcp/releases
2. Drag into Claude Desktop Settings
3. Done. Optionally set a custom prompts folder when prompted.

NPX (auto-updates):

// ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
// %APPDATA%\Claude\claude_desktop_config.json (Windows)
{
  "mcpServers": {
    "claude-prompts": {
      "command": "npx",
      "args": ["-y", "claude-prompts@latest"]
    }
  }
}

Restart Claude Desktop. Test it:

>>research_chain topic:'remote team policies' purpose:'handbook update'

→ Returns a 4-step research workflow with methodology injection and quality gates.

Other MCP Clients

Generic MCP clients (.cursor/mcp.json, etc.)

Add to your MCP config:

{
  "mcpServers": {
    "claude-prompts": {
      "command": "npx",
      "args": ["-y", "claude-prompts@latest"]
    }
  }
}

Test: resource_manager(resource_type:"prompt", action:"list")

Cursor 1-click install Add to Cursor
From Source
git clone https://github.com/minipuft/claude-prompts-mcp.git
cd claude-prompts-mcp/server && npm install && npm run build

Then point your config to server/dist/index.js.

Transport options: --transport=stdio (default), --transport=streamable-http (recommended for HTTP).

Custom Resources

Use your own prompts without cloning:

{
  "mcpServers": {
    "claude-prompts": {
      "command": "npx",
      "args": ["-y", "claude-prompts@latest"],
      "env": {
        "MCP_RESOURCES_PATH": "/path/to/your/resources"
      }
    }
  }
}

Your resources directory can contain: prompts/, gates/, methodologies/, styles/.

Override MethodExample
All resourcesMCP_RESOURCES_PATH=/path/to/resources
Just promptsMCP_PROMPTS_PATH=/path/to/prompts
CLI flag (dev)--prompts=/path/to/prompts

Priority: CLI flags > individual env vars > MCP_RESOURCES_PATH > package defaults.

See CLI Configuration for all options.


What You Get

🔥 Hot Reload

Edit prompts, test immediately. Better yet—ask Claude to fix them:

User: "The code_review prompt is too verbose"
Claude: resource_manager(action:"update", id:"code_review", ...)
User: "Test it"
Claude: prompt_engine(command:">>code_review")  # Uses updated version instantly

🔗 Chains

Break complex tasks into steps with -->:

analyze code --> identify issues --> propose fixes --> generate tests

Each step's output flows to the next. Add quality gates between steps.

🧠 Frameworks

Inject structured thinking patterns:

@CAGEERF Review this architecture    # Context → Analysis → Goals → Execution → Evaluation → Refinement
@ReACT Debug this error              # Reason → Act → Observe loops

🛡️ Gates

Quality criteria Claude self-checks:

Summarize this :: 'under 200 words' :: 'include key statistics'

Failed gates can retry automatically or pause for your decision.

✨ Judge Selection

Let Claude pick the right tools:

%judge Help me refactor this codebase

Claude analyzes available frameworks, gates, and styles, then applies the best combination.

📜 Version History

Every update is versioned. Compare, rollback, undo:

resource_manager(action:"history", id:"code_review")
resource_manager(action:"rollback", id:"code_review", version:2, confirm:true)

Syntax Reference

SymbolNameWhat It DoesExample
>>PromptExecute template>>code_review
-->ChainPipe to next stepstep1 --> step2
@FrameworkInject methodology@CAGEERF
::GateAdd quality criteria:: 'cite sources'
%ModifierToggle behavior%clean, %judge
#StyleApply formatting#analytical

Modifiers:

  • %clean — No framework/gate injection
  • %lean — Gates only, skip framework
  • %guided — Force framework injection
  • %judge — Claude selects best resources

Using Gates

# Inline (quick)
Research AI :: 'use recent sources' --> Summarize :: 'be concise'

# With framework
@CAGEERF Explain React hooks :: 'include examples'

# Programmatic
prompt_engine({
  command: ">>code_review",
  gates: [{ name: "Security", criteria: ["No hardcoded secrets"] }]
})
SeverityBehavior
Critical/HighMust pass (blocking)
Medium/LowWarns, continues (advisory)

See Gates Guide for full schema.


Configuration

Customize via server/config.json:

SectionSettingDefaultDescription
promptsdirectorypromptsPrompts directory (hot-reloaded)
frameworksinjection.systemPromptenabledAuto-inject methodology guidance
gatesdefinitionsDirectorygatesQuality gate definitions
executionjudgetrueEnable %judge resource selection

The Three Tools

ToolPurpose
prompt_engineExecute prompts with frameworks and gates
resource_managerCRUD for prompts, gates, methodologies
system_controlStatus, analytics, health checks
prompt_engine(command:"@CAGEERF >>analysis topic:'AI safety'")
resource_manager(resource_type:"prompt", action:"list")
system_control(action:"status")

Documentation


Contributing

cd server
npm install && npm run build
npm test
npm run validate:all  # Full CI check

See CONTRIBUTING.md for details.


License

MIT

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