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Meta-mcp MCP server guide

Overview​

The meta-mcp server acts as an intelligent intermediary between AI clients and multiple MCP servers. It provides tool discovery, unified access to multiple MCP servers through a single endpoint, and intelligent routing of requests to appropriate MCP tools.

Key features include:

  • Tool discovery: Uses hybrid search (semantic + keyword) to find the right tools for your tasks
  • Unified access: Single endpoint to access all your MCP servers without managing multiple connections
  • Tool management: Seamlessly manage large numbers of MCP tools across different servers
  • Intelligent routing: Automatically routes requests to the appropriate MCP server based on tool requirements

Metadata​

Expand to view the MCP server's metadata
Server metadata
Name: meta-mcp
Type: container
Description: MCP server that acts as an intelligent intermediary between AI clients and multiple MCP servers
Tier: Official
Status: Active
Transport: streamable-http
Image: ghcr.io/stackloklabs/meta-mcp:latest
Has Provenance: Yes

Permissions:
  Network:
Repository URL: https://github.com/StacklokLabs/meta-mcp
Popularity: 0 stars, 0 pulls
Last Updated: 2025-09-15T08:11:53Z

Tools:
  - find_tool
  - call_tool

Tags:
  mcp, proxy, gateway, intelligent

Example Command:
  thv run meta-mcp

Usage​

Prerequisites

You need both the ToolHive CLI and UI to use the meta-mcp server because it depends on the UI's API server for tool discovery and management.

  • ToolHive UI (version >= 0.6.0) must be running for setup
  • ToolHive CLI (version >= 0.3.1)

The meta-mcp server should run in a dedicated group to keep client configurations clean. Other MCP servers that you want to access through meta-mcp should be in a separate group.

Step 1: Create a dedicated group and run meta-mcp

# Create the meta group
thv group create meta

# Run meta-mcp in the dedicated group
thv run --group meta meta-mcp

Step 2: Configure your AI client for the meta group

# Register your AI client with the meta group
thv client setup

# Verify the configuration
thv client list-registered

Step 3: Add MCP servers to the default group

# Add MCP servers that you want to access through meta-mcp
thv run github
thv run filesystem
thv run time

# Verify the configuration - meta-mcp should be in 'meta' group, others in 'default'
thv list

Sample prompts​

Once meta-mcp is configured and running, you can use it with natural language prompts. The server automatically discovers and routes to appropriate tools:

Direct task examples:

  • "Get the details of GitHub issue 1911 from stacklok/toolhive repo"
  • "List recent PRs from stacklok/toolhive repo"

The meta-mcp workflow:

  1. Your AI client sends the request to meta-mcp
  2. Meta-mcp uses hybrid search (semantic + keyword) to find relevant tools across all connected MCP servers
  3. Meta-mcp server returns the short list of matching tools to the client
  4. Client selects one tool from the short list and uses meta-mcp to call that tool
  5. Results are returned from meta-mcp to the client

Available tools​

The meta-mcp server provides two main tools:

find_tool​

Discovers available tools that match your requirements using hybrid search (semantic + keyword).

Parameters:

  • tool_description: Description of the task or capability needed (e.g., "web search", "analyze CSV file")
  • tool_keywords: Space-separated keywords of the task or capability needed (e.g., "list issues github", "SQL query postgres")

call_tool​

Executes a specific tool with provided parameters after discovery.

Parameters:

  • server_name: Name of the MCP server providing the tool
  • tool_name: Name of the tool to execute
  • parameters: Dictionary of arguments required by the tool
  • Use descriptive group names: Keep meta-mcp in a dedicated group to maintain clean client configurations
  • Regular updates: Keep both ToolHive and meta-mcp updated for the latest features and compatibility
Best Practice

Start with a small set of MCP servers in the default group and gradually add more as you become familiar with meta-mcp's tool discovery capabilities. This makes it easier to understand which tools are being used for different tasks.