# mcpctl

**kubectl for MCP servers.** A management system for [Model Context Protocol](https://modelcontextprotocol.io) servers — define, deploy, and connect MCP servers to Claude using familiar kubectl-style commands.

```
mcpctl get servers
NAME             TRANSPORT   REPLICAS   DOCKER IMAGE                              DESCRIPTION
grafana          STDIO       1          grafana/mcp-grafana:latest                Grafana MCP server
home-assistant   SSE         1          ghcr.io/homeassistant-ai/ha-mcp:latest    Home Assistant MCP
docmost          SSE         1          10.0.0.194:3012/michal/docmost-mcp:latest Docmost wiki MCP
```

## What is this?

mcpctl manages MCP servers the same way kubectl manages Kubernetes pods. You define servers declaratively in YAML, group them into projects, and connect them to Claude Code or any MCP client through a local proxy.

**The architecture:**

```
Claude Code  <--STDIO-->  mcplocal (local proxy)  <--HTTP-->  mcpd (daemon)  <--Docker-->  MCP servers
```

- **mcpd** — the daemon. Runs on a server, manages MCP server containers (Docker/Podman), stores configuration in PostgreSQL.
- **mcplocal** — local proxy. Runs on your machine, presents a single MCP endpoint to Claude that merges tools from all your servers. Handles namespacing (`grafana/search_dashboards`), gated sessions, and prompt delivery.
- **mcpctl** — the CLI. Talks to mcpd (via mcplocal or directly) to manage everything.

## Quick Start

### 1. Install

```bash
# From RPM repository
sudo dnf config-manager --add-repo https://your-registry/api/packages/mcpctl/rpm.repo
sudo dnf install mcpctl

# Or build from source
git clone https://github.com/your-org/mcpctl.git
cd mcpctl
pnpm install
pnpm build
pnpm rpm:build   # requires bun and nfpm
```

### 2. Connect to a daemon

```bash
# Login to an mcpd instance
mcpctl login --mcpd-url http://your-server:3000

# Check connectivity
mcpctl status
```

### 3. Create your first secret

Secrets store credentials that servers need — API tokens, passwords, etc.

```bash
mcpctl create secret grafana-token \
  --data TOKEN=glsa_xxxxxxxxxxxx
```

### 4. Create your first server

A server is an MCP server definition — what Docker image to run, what transport it speaks, what environment it needs.

```bash
mcpctl create server grafana \
  --docker-image grafana/mcp-grafana:latest \
  --transport STDIO \
  --env GRAFANA_URL=http://grafana.local:3000 \
  --env GRAFANA_AUTH_TOKEN=secretRef:grafana-token:TOKEN
```

mcpd pulls the image, starts a container, and keeps it running. Check on it:

```bash
mcpctl get instances         # See running containers
mcpctl logs grafana          # View server logs
mcpctl describe server grafana  # Full details
```

### 5. Create a project

A project groups servers together and configures how Claude interacts with them.

```bash
mcpctl create project monitoring \
  --description "Grafana dashboards and alerting" \
  --server grafana \
  --no-gated
```

### 6. Connect Claude Code

Generate the `.mcp.json` config for Claude Code:

```bash
mcpctl config claude --project monitoring
```

This writes a `.mcp.json` that tells Claude Code to connect through mcplocal. Restart Claude Code and your Grafana tools appear:

```
mcpctl console monitoring   # Preview what Claude sees
```

## Declarative Configuration

Everything can be defined in YAML and applied with `mcpctl apply`:

```yaml
# infrastructure.yaml
secrets:
  - name: grafana-token
    data:
      TOKEN: "glsa_xxxxxxxxxxxx"

servers:
  - name: grafana
    description: "Grafana dashboards and alerting"
    dockerImage: grafana/mcp-grafana:latest
    transport: STDIO
    env:
      - name: GRAFANA_URL
        value: "http://grafana.local:3000"
      - name: GRAFANA_AUTH_TOKEN
        valueFrom:
          secretRef:
            name: grafana-token
            key: TOKEN

projects:
  - name: monitoring
    description: "Infrastructure monitoring"
    gated: false
    servers:
      - grafana
```

```bash
mcpctl apply -f infrastructure.yaml
```

Round-trip works too — export, edit, re-apply:

```bash
mcpctl get all --project monitoring -o yaml > backup.yaml
# edit backup.yaml...
mcpctl apply -f backup.yaml
```

## Content Pipeline (ProxyModel)

ProxyModel defines a **content transformation pipeline** that runs between upstream MCP servers and the client (e.g., Claude). It processes tool results, prompts, and resources through ordered stages before delivering them.

### Built-in Models

| Model | Stages | Use case |
|-------|--------|----------|
| **default** | `passthrough` → `paginate` (8KB pages) | Safe pass-through with pagination for large responses |
| **subindex** | `section-split` → `summarize-tree` | Splits large content into sections, returns a summary index. Client drills down with `_resultId`/`_section` params |

### How `subindex` Works

1. Upstream returns a large tool result (e.g., 50KB of device states)
2. `section-split` divides content into logical sections (2KB–15KB each)
3. `summarize-tree` generates a compact index with section summaries (~200 tokens each)
4. Client receives the index and can request specific sections via `_section` parameter

### Configuration

Set per-project (all servers use the same model):

```yaml
kind: Project
metadata:
  name: home-automation
spec:
  servers: [home-assistant, node-red]
  proxyModel: subindex
```

Override per-server within a project:

```yaml
kind: Project
metadata:
  name: monitoring
spec:
  servers: [grafana, prometheus]
  proxyModel: default
  serverOverrides:
    grafana:
      proxyModel: subindex
```

Via CLI:

```bash
mcpctl create project monitoring --server grafana --server prometheus --proxy-model subindex
```

### Custom ProxyModels

Place YAML files in `~/.mcpctl/proxymodels/` to define custom pipelines:

```yaml
kind: ProxyModel
metadata:
  name: my-pipeline
spec:
  stages:
    - type: section-split
      config:
        minSectionSize: 1000
        maxSectionSize: 10000
    - type: summarize-tree
      config:
        maxTokens: 150
        maxDepth: 2
  appliesTo: [toolResult, prompt]
  cacheable: true
```

Inspect available models: `mcpctl get proxymodels` / `mcpctl describe proxymodel subindex`

## Resources

| Resource | What it is | Example |
|----------|-----------|---------|
| **server** | MCP server definition | Docker image + transport + env vars |
| **instance** | Running container (immutable) | Auto-created from server replicas |
| **secret** | Key-value credentials | API tokens, passwords |
| **template** | Reusable server blueprint | Community server configs |
| **project** | Workspace grouping servers | "monitoring", "home-automation" |
| **prompt** | Curated content for Claude | Instructions, docs, guides |
| **promptrequest** | Pending prompt proposal | LLM-submitted, needs approval |
| **rbac** | Access control bindings | Who can do what |
| **serverattachment** | Server-to-project link | Virtual resource for `apply` |

## Commands

```bash
# List resources
mcpctl get servers
mcpctl get instances
mcpctl get projects
mcpctl get prompts --project myproject

# Detailed view
mcpctl describe server grafana
mcpctl describe project monitoring

# Create resources
mcpctl create server <name> [flags]
mcpctl create secret <name> --data KEY=value
mcpctl create project <name> --server <srv> [--gated]
mcpctl create prompt <name> --project <proj> --content "..."

# Modify resources
mcpctl edit server grafana          # Opens in $EDITOR
mcpctl patch project myproj gated=true
mcpctl apply -f config.yaml         # Declarative create/update

# Delete resources
mcpctl delete server grafana

# Logs and debugging
mcpctl logs grafana                 # Container logs
mcpctl console monitoring           # Interactive MCP console
mcpctl console --inspect            # Traffic inspector

# Backup and restore
mcpctl backup -o backup.json
mcpctl restore -i backup.json

# Project management
mcpctl --project monitoring get servers       # Project-scoped listing
mcpctl --project monitoring attach-server grafana
mcpctl --project monitoring detach-server grafana
```

## Templates

Templates are reusable server configurations. Create a server from a template without repeating all the config:

```bash
# Register a template
mcpctl create template home-assistant \
  --docker-image "ghcr.io/homeassistant-ai/ha-mcp:latest" \
  --transport SSE \
  --container-port 8086

# Create a server from it
mcpctl create server my-ha \
  --from-template home-assistant \
  --env-from-secret ha-secrets
```

## Gated Sessions

Projects are **gated** by default. When Claude connects to a gated project:

1. Claude sees only a `begin_session` tool initially
2. Claude calls `begin_session` with a description of its task
3. mcplocal matches relevant prompts and delivers them
4. The full tool list is revealed

This keeps Claude's context focused — instead of dumping 100+ tools and pages of docs upfront, only the relevant ones are delivered based on the task at hand.

```bash
# Enable/disable gating
mcpctl patch project monitoring gated=true
mcpctl patch project monitoring gated=false
```

## Prompts

Prompts are curated content delivered to Claude through the MCP protocol. They can be plain text or linked to external MCP resources (like wiki pages).

```bash
# Create a text prompt
mcpctl create prompt deployment-guide \
  --project monitoring \
  --content-file docs/deployment.md \
  --priority 7

# Create a linked prompt (content fetched live from an MCP resource)
mcpctl create prompt wiki-page \
  --project monitoring \
  --link "monitoring/docmost:docmost://pages/abc123" \
  --priority 5
```

Claude can also **propose** prompts during a session. These appear as prompt requests that you can review and approve:

```bash
mcpctl get promptrequests
mcpctl approve promptrequest proposed-guide
```

## Interactive Console

The console lets you see exactly what Claude sees — tools, resources, prompts — and call tools interactively:

```bash
mcpctl console monitoring
```

The traffic inspector watches MCP traffic from other clients in real-time:

```bash
mcpctl console --inspect
```

## Architecture

```
┌──────────────┐           ┌─────────────────────────────────────────┐
│ Claude Code  │   STDIO   │           mcplocal (proxy)              │
│              │◄─────────►│                                         │
│ (or any MCP  │           │  Namespace-merging MCP proxy            │
│  client)     │           │  Gated sessions + prompt delivery       │
│              │           │  Per-project endpoints                  │
└──────────────┘           │  Traffic inspection                     │
                           └──────────────┬──────────────────────────┘
                                          │ HTTP (REST + MCP proxy)
                                          │
                           ┌──────────────┴──────────────────────────┐
                           │              mcpd (daemon)              │
                           │                                         │
                           │  REST API (/api/v1/*)                   │
                           │  MCP proxy (routes tool calls)          │
                           │  PostgreSQL (Prisma ORM)                │
                           │  Docker/Podman container management     │
                           │  Health probes (STDIO, SSE, HTTP)       │
                           │  RBAC enforcement                       │
                           │                                         │
                           │  ┌───────────────────────────────────┐  │
                           │  │     MCP Server Containers         │  │
                           │  │                                   │  │
                           │  │  grafana/  home-assistant/  ...   │  │
                           │  │  (managed + proxied by mcpd)      │  │
                           │  └───────────────────────────────────┘  │
                           └─────────────────────────────────────────┘
```

Clients never connect to MCP server containers directly — all tool calls go through mcplocal → mcpd, which proxies them to the right container via STDIO/SSE/HTTP. This keeps containers unexposed and lets mcpd enforce RBAC and health checks.

**Tool namespacing**: When Claude connects to a project with servers `grafana` and `slack`, it sees tools like `grafana/search_dashboards` and `slack/send_message`. mcplocal routes each call through mcpd to the correct upstream server.

## Project Structure

```
mcpctl/
├── src/
│   ├── cli/          # mcpctl command-line interface (Commander.js)
│   ├── mcpd/         # Daemon server (Fastify 5, REST API)
│   ├── mcplocal/     # Local MCP proxy (namespace merging, gating)
│   ├── db/           # Database schema (Prisma) and migrations
│   └── shared/       # Shared types and utilities
├── deploy/           # Docker Compose for local development
├── stack/            # Production deployment (Portainer)
├── scripts/          # Build, release, and deploy scripts
├── examples/         # Example YAML configurations
└── completions/      # Shell completions (fish, bash)
```

## Development

```bash
# Prerequisites: Node.js 20+, pnpm 9+, Docker/Podman

# Install dependencies
pnpm install

# Start local database
pnpm db:up

# Generate Prisma client
cd src/db && npx prisma generate && cd ../..

# Build all packages
pnpm build

# Run tests
pnpm test:run

# Development mode (mcpd with hot-reload)
cd src/mcpd && pnpm dev
```

## License

MIT