slop-mcp 0.12.1

MCP orchestrator — connect unlimited Model Context Protocol servers through 8 meta-tools. Progressive tool discovery keeps your AI agent's context window small.

slop-mcp

Install many MCPs without killing your context.

slop-mcp is an MCP orchestrator that lets you connect dozens of MCP servers while exposing only 8 meta-tools to your agent. No more context window bloat from loading hundreds of tool definitions upfront.

Without slop-mcp:  50 MCPs × 20 tools = 1000 tool definitions in context
With slop-mcp:     50 MCPs × 20 tools = 8 tool definitions in context

Documentation

• Quick Start Guide - Get running in 5 minutes
• Full Documentation - Complete guides and reference
• KDL Configuration - Configuration reference
• CLI Reference - Command-line options

The Problem

As described in Anthropic's article Code Execution with MCP, current MCP implementations face two critical challenges:

1. Context Window Overload: When agents connect to many tools, loading all tool definitions upfront consumes excessive tokens. With thousands of connected tools, agents must process hundreds of thousands of tokens before even reading user requests.

2. Intermediate Result Duplication: Tool outputs repeatedly flow through the model's context. Transferring large documents between services forces the same data through the model between operations, potentially doubling token consumption.

The article proposes code execution within MCP as a solution—letting agents discover tools progressively and process data within the execution environment rather than shuttling everything through context.

How slop-mcp Addresses These Issues

slop-mcp takes a different but complementary approach: instead of code execution, it provides an orchestration layer that aggregates multiple MCP servers while maintaining context efficiency.

Progressive Tool Discovery

Rather than loading all tool definitions upfront, slop-mcp exposes just 8 meta-tools:

Tool	Purpose
search_tools	Find tools across all connected MCPs by name or description
execute_tool	Execute a specific tool on a specific MCP
get_metadata	Get full metadata (tools, prompts, resources) for connected MCPs
run_slop	Execute SLOP scripts with access to all MCPs
manage_mcps	Register/unregister MCPs at runtime
auth_mcp	Handle OAuth authentication for MCPs that require it
slop_reference	Search SLOP built-in functions by name or category
slop_help	Get full details for a specific SLOP function

This means an agent connecting to slop-mcp sees 8 tool definitions regardless of how many MCPs are connected or how many tools they expose. The agent discovers tools on-demand via search_tools and executes them via execute_tool.

Lazy Connection & Async Startup

MCP servers connect asynchronously in the background:

Server starts → Immediately ready to serve
                ↓ (background)
                MCP #1 connecting...
                MCP #2 connecting...
                MCP #N connecting...

The server doesn't block waiting for all MCPs to connect. Tools become available progressively as their MCPs come online.

In-Environment Script Execution

The run_slop tool allows executing structured scripts that can:

• Call multiple tools across different MCPs
• Process intermediate results without sending them back through the model
• Chain operations efficiently

This keeps large intermediate data within the execution environment, addressing the token duplication problem.

Efficient Tool Index

Tools are indexed locally when MCPs connect:

• Fuzzy search by name or description
• Filter by MCP name
• No network calls during search
• Thread-safe concurrent access

Architecture

┌─────────────────────────────────────────────────────┐
│              slop-mcp Server                        │
│  ┌───────────────────────────────────────────────┐  │
│  │  8 Meta-Tools (constant context cost)         │  │
│  │  • search_tools    • execute_tool             │  │
│  │  • get_metadata    • run_slop                 │  │
│  │  • manage_mcps     • auth_mcp                 │  │
│  │  • slop_reference  • slop_help                │  │
│  └───────────────────────────────────────────────┘  │
│                          │                          │
│         ┌────────────────┼────────────────┐         │
│         ▼                ▼                ▼         │
│  ┌────────────┐  ┌────────────┐  ┌────────────┐    │
│  │  Registry  │  │ Tool Index │  │   Auth     │    │
│  │  (async)   │  │  (local)   │  │  (OAuth)   │    │
│  └─────┬──────┘  └────────────┘  └────────────┘    │
└────────┼────────────────────────────────────────────┘
         │
    ┌────┼────┬─────────────┐
    ▼    ▼    ▼             ▼
┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐
│MCP #1│ │MCP #2│ │MCP #3│ │MCP #N│
│stdio │ │ SSE  │ │ HTTP │ │ ...  │
└──────┘ └──────┘ └──────┘ └──────┘

Configuration

slop-mcp uses KDL configuration with three-tier scoping:

Scope	File	Purpose
User	~/.config/slop-mcp/config.kdl	Cross-project defaults
Project	.slop-mcp.kdl	Git-tracked project config
Local	.slop-mcp.local.kdl	Git-ignored secrets

Example configuration:

mcp "filesystem" {
    command "npx" "-y" "@anthropic/mcp-filesystem"
    args "/path/to/allowed/dir"
}

mcp "github" {
    transport "sse"
    url "https://mcp.github.com/sse"
    // OAuth handled automatically via auth_mcp tool
}

Import existing configurations:

import "claude-desktop"  // Import from Claude Desktop config
import "claude-code"     // Import from Claude Code settings

Quick Start

npm

npx @standardbeagle/slop-mcp

PyPI

uvx slop-mcp

Or install globally:

# npm
npm install -g @standardbeagle/slop-mcp

# pip
pip install slop-mcp

From Source

go install github.com/standardbeagle/slop-mcp/cmd/slop-mcp@latest

Usage

As an MCP Server (stdio)

slop-mcp serve

With HTTP/SSE Transport

slop-mcp serve --port 8080

Claude Desktop Configuration

Add to your Claude Desktop config:

{
  "mcpServers": {
    "slop": {
      "command": "slop-mcp",
      "args": ["serve"]
    }
  }
}

Comparison with Code Execution Approach

Aspect	Code Execution (Article)	slop-mcp
Tool Discovery	Filesystem exploration	search_tools with fuzzy matching
Context Cost	Minimal (code interpreter)	Constant (8 meta-tools)
Data Processing	In-sandbox code	SLOP scripts via run_slop
Infrastructure	Secure sandbox required	Standard MCP servers
Flexibility	Full code execution	Structured tool orchestration

Both approaches solve the same core problems. Code execution offers maximum flexibility but requires sandboxing infrastructure. slop-mcp provides a simpler deployment model while still achieving significant context efficiency gains.

Related Projects

• standardbeagle-tools - Claude Code plugin for slop-mcp integration

License

MIT