How do I install MCP Wrapper Rs?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does MCP Wrapper Rs need an API key?

No required environment variable is listed on this record.

What transport does MCP Wrapper Rs use?

MCP Wrapper Rs runs locally over stdio.

When was MCP Wrapper Rs last checked?

Last checked 2026-10-28. Stable MCP supervisor proxy for stdio servers: cached discovery, unified mcp.wrapper lifecycle control, and on-demand backend processes.

เซิร์ฟเวอร์ MCP MCP Wrapper Rs

Power your agent

เพิ่มเซิร์ฟเวอร์นี้

Install from the publisher. Not a PlugMCP endpoint.

รันบน PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

รันบน PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

เปิด

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-28

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

mcp-wrapper-rs

A stable MCP supervisor proxy written in Rust. It turns stdio MCP servers into controllable, observable backends by caching stable discovery data, exposing lifecycle control through the unified mcp.wrapper tool, and starting or reusing backend subprocesses only when dynamic calls need them.

中文文檔

The Problem

MCP servers (especially npx-based ones) consume significant memory when running persistently:

npx -y mcp-searxng          ~100MB
npx -y fetcher-mcp          ~120MB
npx -y @oevortex/ddg_search ~100MB
python3 server.py           ~50MB
────────────────────────────────────
Total                       ~370MB (idle!)

The Solution

mcp-wrapper-rs acts as a stable supervisor proxy:

┌─────────────┐      ┌──────────────────────┐      ┌─────────────┐
│ Claude Code │ ──── │ mcp-wrapper-rs       │ ──── │ MCP Server  │
│             │      │ cache + mcp.wrapper  │      │ (on-demand) │
└─────────────┘      └──────────────────────┘      └─────────────┘

Startup: Spawns the backend once to cache stable discovery data and exposes wrapper-owned tools capability
Runtime: Serves initialize and list requests from cache, including the wrapper-owned mcp.wrapper tool
Wrapper control: Handles mcp.wrapper actions locally for backend status, ping, refresh, restart, and stop
Backend calls: Keeps dynamic MCP work owned by the backend subprocess, reusing or respawning it as needed

Result: 4 MCP servers using only ~8MB total (vs ~370MB before)

Core Concepts

mcp-wrapper-rs is a supervisor proxy: a stable MCP server the client connects to, behind which real MCP servers are managed children. The client talks to one transport that never dies; backend processes start, restart, and stop without ever breaking that connection.

Four ideas define the product:

Cache-first idle economy. Stable discovery data (initialize, tools/list, prompts/list, resources/list) is captured once and served from memory. Backends are spawned lazily — only when a dynamic call (tools/call, resources/read, …) actually needs them — so an idle wrapper costs almost nothing. The externally served tools/list / initialize views are hold-state: rebuilt only when backend data loads or refreshes, never recomputed per request. A refresh validates every selected discovery result into one candidate and atomically replaces the committed snapshot; errors, stale revisions, and backend-generation changes preserve the previous truth.
One unified control surface: mcp.wrapper. The wrapper injects exactly one reserved tool, mcp.wrapper, into tools/list. Backend status, ping, discovery refresh, restart, and stop are all actions on this single tool. The admin CLI and MCP clients project into the same action schema and result envelope — there is no second control path. A backend that tries to register the reserved name cannot overwrite it.
Owner-local lifecycle. Each piece of state has exactly one owner. BackendSlot owns the backend process — its alive decision, restart/stop transitions, and the active-call safety invariant. RequestLifetime owns request identity as session-aware rows and derives active-call state from those rows, so cancellation, completion, and backend generation cannot be confused across clients. Cache owns discovery data and the external view lifetime. tools is the external entry and dispatch layer; it owns the action schema and envelope but holds no lifecycle state — it routes actions to owner methods. Validation lives on the invocation objects themselves, not in a separate validator.
Observe → hook → readback control plane. Backend facts (process exit, listChanged, degraded cooldown) are signals, not truth. A control action requests a legal transition through the owner; the new state is proven by a readback snapshot before any client, log, CLI, or tool projection reports it. Status is evidence, not a hope attached to a command.

In daemon mode, one broker owns a single shared backend/cache pair and fans out to many client sessions. Client identity is the composite (session_id, client_request_id) rather than a process-global JSON-RPC ID, and the broker joins it to backend request identity and generation. Active-call safety is derived from those live request rows, so one session cannot cancel another session's request or stop/restart the shared backend while work is in flight.

What it fills in over plain stdio MCP

Plain stdio MCP leaves real gaps. This wrapper closes them behind one stable transport:

Gap in plain stdio MCP	What mcp-wrapper-rs adds
Client cannot control the server it is connected to	`mcp.wrapper` lifecycle actions: `restart` / `stop` / `refresh` / `status` / `ping`, with the transport staying alive across restarts
No standard backend-state readback	`observe → hook → readback` control plane: snapshot, generation, cache epoch, discovery hash as proven evidence
Idle servers keep consuming memory	Cache-first stable discovery layer + lazy backend spawn
Every client spawns its own server	Daemon broker shares one backend/cache pair across sessions

Next direction: a deeper bidirectional communication bridge, so wrapped servers can drive richer two-way (server-to-client) MCP interactions through the stable wrapper transport.

Full architecture, owner map, flow projections, and proof gates live in the handbook.

Installation

From Source

git clone https://github.com/woolkingx/mcp-wrapper-rs.git
cd mcp-wrapper-rs
cargo install --path .

This will compile in release mode and install the binary to ~/.cargo/bin/mcp-wrapper-rs (~440KB).

For development: Use cargo build --release to build without installing. Binary will be at target/release/mcp-wrapper-rs.

Pre-built Binaries

Check Releases for pre-built binaries.

Usage

mcp-wrapper-rs [options] <command> [args...]

Options

Flag	Description
`--init-timeout <secs>`	Seconds to wait for subprocess init handshake (default: 30)
`--daemon`	Share a single backend via a broker process (N:1 architecture)
`--version`, `-V`	Show version and exit
`--help`, `-h`	Show help and exit

Examples

# Wrap an npx-based MCP server
mcp-wrapper-rs npx -y mcp-searxng

# Wrap a Python MCP server
mcp-wrapper-rs python3 /path/to/server.py

# Wrap with environment variables (inherited from parent)
SEARXNG_URL=http://localhost:8080 mcp-wrapper-rs npx -y mcp-searxng

# Use custom init timeout (default: 30s; increase for slow-starting servers)
mcp-wrapper-rs --init-timeout 15 npx -y mcp-searxng

# Daemon mode: multiple clients share one backend
mcp-wrapper-rs --daemon python3 /path/to/server.py

Daemon Mode

With --daemon, multiple wrapper instances share a single backend subprocess through an auto-managed broker process:

wrapper 1 (stdio) ──→ UDS ──→ broker ──→ backend (single subprocess)
wrapper 2 (stdio) ──→ UDS ──→ broker ──↗
wrapper 3 (stdio) ──→ UDS ──→ broker ──↗

The first wrapper auto-spawns the broker; subsequent wrappers connect to it. The broker manages:

Shared cache (initialize, tools/list, etc.)
Backend subprocess lifecycle (lazy spawn, respawn on failure)
Session-aware request multiplexing with generation-safe ID remapping
Notification fanout to all connected clients
Graceful shutdown: broker spawned via --daemon runs until explicit SIGTERM

Coordination uses flock + PID file + Unix domain socket under $XDG_RUNTIME_DIR/mcp-wrapper/.

When to use: Stateless MCP servers shared across multiple Claude Code sessions. When NOT to use: Stateful servers (e.g., browser automation) where each session needs its own backend.

Admin CLI

The admin CLI is a projection over the same mcp.wrapper action schema, daemon, broker, backend, and MCP cache owners used at runtime. It does not manage backend processes through a second code path.

# Read broker/backend status for a command identity
mcp-wrapper-rs status --json -- python3 /path/to/server.py

# Start the broker if needed, then restart the backend and refresh MCP cache data
mcp-wrapper-rs backend restart --start --json -- python3 /path/to/server.py

# Inspect or stop the broker for the command identity
mcp-wrapper-rs broker status --json -- python3 /path/to/server.py
mcp-wrapper-rs broker stop --json -- python3 /path/to/server.py

Read-only admin commands do not start a broker unless --start is present.

Claude Code Configuration

Edit ~/.claude.json:

{
  "mcpServers": {
    "searxng": {
      "type": "stdio",
      "command": "/path/to/mcp-wrapper-rs",
      "args": ["npx", "-y", "mcp-searxng"],
      "env": {
        "SEARXNG_URL": "http://localhost:8080"
      }
    },
    "fetcher": {
      "type": "stdio",
      "command": "/path/to/mcp-wrapper-rs",
      "args": ["npx", "-y", "fetcher-mcp"]
    },
    "my-python-server": {
      "type": "stdio",
      "command": "/path/to/mcp-wrapper-rs",
      "args": ["--daemon", "python3", "/path/to/server.py"]
    }
  }
}

How It Works

Initialization Phase
- Spawns subprocess, performs raw JSON-RPC MCP handshake
- Queries tools/list, prompts/list, resources/list, resources/templates/list
- Each query has a configurable timeout (--init-timeout, default 30s); unresponsive servers are skipped
- Caches all results, kills init subprocess
Runtime Phase
- initialize → Instant response from cache
- tools/list, prompts/list, resources/list → Instant response from cache
- tools/call with mcp.wrapper → Local wrapper control/readback through src/tools
- Other tools/call, resources/read, prompts/get → Forwarded to persistent backend subprocess
- Notifications relayed bidirectionally between client and backend
Resource Management
- Persistent backend subprocess reused across calls
- Dead backend auto-respawns with exponential backoff retry
- Cache invalidation on listChanged notifications

Debug Logging

Debug logging is disabled by default. Enable it with the MCP_WRAPPER_DEBUG environment variable:

MCP_WRAPPER_DEBUG=1 mcp-wrapper-rs npx -y mcp-searxng

Each MCP server gets its own log file based on the inferred name. Log location follows XDG Base Directory spec:

$XDG_RUNTIME_DIR/mcp-wrapper/mcp-searxng.log (Linux with XDG runtime dir)
$TMPDIR/mcp-wrapper/mcp-searxng.log (macOS or custom TMPDIR)
/tmp/mcp-wrapper/mcp-searxng.log (fallback)

You can override the name with MCP_SERVER_NAME:

MCP_SERVER_NAME=my-custom-name mcp-wrapper-rs python3 server.py
# Logs to: $XDG_RUNTIME_DIR/mcp-wrapper/my-custom-name.log

Performance

Metric	Before	After
Memory (4 servers)	~370MB	~8MB
Binary size	N/A	~1.6MB
`tools/list` latency	~2s	<1ms
`tools/call` latency	Same	Same

Real-World Impact

After deploying mcp-wrapper-rs with 8 MCP servers in production:

Startup Performance

Claude Code launch time: ~40% faster
MCP initialization: From ~10s to <1s (instant cache responses)

Runtime Performance

CPU load: Reduced by ~40% (no idle MCP processes)
Response latency: Protocol queries return instantly from cache
Static protocol requests stay in the wrapper cache; backend lifecycle starts only after dynamic MCP work is requested

Why It's Faster

Lazy backend lifecycle: Backend processes only run when needed, eliminating idle overhead
Cache-first design: initialize, tools/list, prompts/list, resources/list served from memory
Unified control surface: CLI and MCP clients use the same wrapper action boundary for lifecycle readback and control
Clean resource lifecycle: Backend process groups are cleaned up when the wrapper stops or explicitly stops the backend

Compatibility

Works with any MCP server that:

Uses stdio transport
Follows MCP protocol (JSON-RPC 2.0)
Supports standard initialization handshake

Tested with:

npx -y mcp-searxng
npx -y fetcher-mcp
npx -y @oevortex/ddg_search
Python-based MCP servers

Architecture

Start with the handbook for the current architecture map, owner boundaries, flow projections, and proof gates. See ARCHITECTURE.md for the older compact architecture note.

License

MIT License - see LICENSE

Contributing

Contributions welcome. Please read the handbook first to understand the current owner boundaries and proof gates.

เพิ่มเซิร์ฟเวอร์นี้

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install MCP Wrapper Rs?

Does MCP Wrapper Rs need an API key?

What transport does MCP Wrapper Rs use?

When was MCP Wrapper Rs last checked?

Documentation

mcp-wrapper-rs

The Problem

The Solution

Core Concepts

What it fills in over plain stdio MCP

Installation

From Source

Pre-built Binaries

Usage

Options

Examples

Daemon Mode

Admin CLI

Claude Code Configuration

How It Works

Debug Logging

Performance

Real-World Impact

Compatibility

Architecture

License

Contributing