Enhancing Model Context Protocol (MCP) with Dynamic Tool Discovery for Smarter AI

The Model Context Protocol (MCP) is an emerging standard that enables structured data provisioning for LLMs and AI agents. However, the current data discovery mechanism in MCP is static. This limits the AI’s ability to dynamically assess the utility, relevance, and efficiency of data tool calls in real time. Here I present an enhancement to MCP "tool discovery" that introduces dynamic data descriptions, allowing LLM to be better informed.

Python, LLM

This talk will introduce the concept of MCP with a demonstration of how MCP clients and servers. How they are built and how they interact.

Then I will focus on a specific flow - the tool discovery. It is possible to enhance the specification of the MCP allowing tools to update their metadata periodically based on real-time system and environmental factors. This enhancement will enable AI models to intelligently choose tools based on:

-Data freshness (last update, data volume, change frequency) -System load & latency (server utilization, estimated response time) -API rate limits & costs (quota usage, request cost) -Geographical & time-based relevance (regional availability, peak usage) -Data accuracy & trustworthiness (confidence scores, bias detection)

These are highlighted to encourage the audience to think of enhance the MCP in more ways.