MCP and Tool Usage in AI

How MCP Works

MCP (Model Context Protocol) is a framework that allows AI models to dynamically interact with external tools by:

1. Tool Registry: Maintains a catalog of available tools with metadata (e.g., purpose, input/output schemas).
2. Context Analysis: The model evaluates the user query and current context to determine if a tool is needed. For example:
   • Query: “What’s the weather in Tokyo tomorrow?” → Triggers a weather API tool.
   • Query: “Solve \(x^2 + 3x - 4 = 0\)” → Triggers a symbolic math solver.
3. Tool Selection: Uses embeddings or heuristic rules to match the query intent with the best tool. DeepSeek might rank tools by:
   \(\text{Score}(t) = \alpha \cdot \text{Relevance}(t, q) + \beta \cdot \text{Confidence}(t)\)
   where \(t\) = tool, \(q\) = query, and \(\alpha, \beta\) are weights.
4. Execution: The model formats the input for the tool, executes it, and integrates the response into its output.

Does Fine-Tuning Help?

• Pre-trained models (e.g., DeepSeek) can use tools without fine-tuning if they’re trained on tool-calling demonstrations.
• Fine-tuning improves accuracy by:
  • Adapting to proprietary tools (e.g., internal databases).
  • Reducing hallucination in tool selection.
  • Optimizing input/output parsing (e.g., handling API errors).

DeepSeek Example

When you ask DeepSeek to “Book a flight to Paris,” it:

1. Detects intent (travel booking).
2. Selects a flight-booking tool (if available in its registry).
3. Generates structured parameters (dates, budget) for the API.
4. Returns the API’s response in natural language.

Key challenges include handling tool failures and maintaining user context across multi-step workflows.