Mohak8529/Agentic-MCP-Server
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The Model Context Protocol (MCP) server is designed to efficiently manage and execute tasks by coordinating various tools and resources, ensuring scalability and reliability.
Agentic-MCP-Server
MCP Flow
Here’s the overall MCP flow:
Sequential Dependent Flow
Concurrent Independent Flow
##Scaling: 1.) Asychronous execution of tools in case of independedent tasks 2.) Making each tool its own microservice(will be called through API), and make the mcp server just a coordinator rather than being a compute-heavy resource and also tools can be scaled independently 3.) Persistent storage of tasks state input output should be stored, maybe in Redis(as it allows caching by LRU),it helps in recovery in cases of crash and its a general good practice to handle huge traffic of users with different agent states.
##Step by step for scaling Step-by-step:
User sends a task → MCP server
MCP decides subtasks → puts them in async queue
Tool workers execute subtasks → save results in persistent store
MCP collects results → creates final answer
MCP sends answer back to the user
SAMPLING: To integrate sampling in your MCP setup, modify the server to send LLM prompts as sampling requests via FastMCP to the client, which forwards them to the GROQ gRPC service with user approval. This ensures MCP compliance, enhances security through user oversight, and enables LLM-agnostic flexibility for scalable, interoperable workflows.