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Run multiple labeled instances of one MCP server without tool name collisions.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCPlexer" yet — see the docs or source repo.
I need to connect to test and production instances of the same MCP server at the same time. Use MCPlexer to assign clear labels to both instances and prevent tool name collisions.
A proxy setup that distinguishes multiple instances by labels so similar tools can be used together without name conflicts.
Help me design an MCPlexer proxy setup that labels two instances of the same MCP server as "internal library" and "external library," ensuring their tool calls are not confused.
A labeled isolation scheme for the toolsets, making their origins easy to distinguish in one client.
I want to aggregate multiple instances of the same MCP server type behind one entry point. Explain how MCPlexer can prevent overlapping tools from overwriting each other.
An explanation of using a proxy and labels to manage repeated instances, focusing on avoiding tool name collisions.
When developers or DevOps engineers need to connect to different environments of the same MCP server at once, labeled instances help avoid duplicate tool names and reduce switching errors.
When multiple instances of the same MCP server must be connected to one client, MCPlexer can act as a proxy and distinguish each tool source through labels.
It is a tiny MCP proxy for running multiple labeled instances of the same MCP server while avoiding tool name collisions.
It is suitable when you need to connect multiple instances of the same MCP server at once, such as parallel use across different environments, configurations, or data sources.
The provided material does not include installation or configuration details; see the source repository.
Aggregate multiple MCP servers into one for search, parallel calls, and orchestration.
Proxy multiple MCP servers while reducing token usage with on-demand tool loading.
Organize flat MCP tools into subcommands to save tokens and improve discovery.
Manage multiple MCP servers and load tool schemas on demand efficiently.
Aggregate multiple MCP servers into a few fixed tools for AI apps.
Aggregate multiple MCP servers into one endpoint for unified LLM access.