Modern agent frameworks increasingly use MCP (Model Context Protocol) to standardize how AI agents interact with external tools, memory systems, and APIs. Instead of embedding tool logic directly inside the agent runtime, MCP introduces a separate tool server layer that exposes capabilities via a common protocol.
The protocol acts like a universal interface between LLMs and external systems, enabling agents to access files, APIs, databases, or automation scripts in a consistent way. MCP was introduced as an open standard in 2024 to simplify tool integration across AI applications. (Google Cloud)
An MCP-based architecture separates the reasoning system (LLM) from the tool execution layer.
The runtime managing the assistant.
Responsibilities:
The reasoning component.
It:
Library integrated in the agent.
Functions:
External process exposing capabilities through MCP.
Typical examples:
Actual implementations that perform real work.
Examples:
Important principle:
Agents never call tools directly — they communicate through the MCP protocol.
Before tools can be used, the MCP client and server negotiate capabilities.
1. Agent startup
The agent launches its MCP client and connects to one or more MCP servers (via stdio, HTTP, or WebSocket).
2. Protocol negotiation
The client sends:
initialize
The server returns:
MCP is a stateful protocol, meaning both sides negotiate capabilities during initialization. (Model Context Protocol)
3. Tool discovery
The client requests:
listTools
The server responds with a list of tools and their metadata.
4. Schema registration
Each tool provides:
5. Tool registry ready
The agent injects these tool schemas into the LLM prompt context.
This enables dynamic tool discovery — agents can connect to new MCP servers and automatically gain new capabilities.
Example task:
User: "Find the latest Linux kernel version."
The agent sends the request to the LLM along with available MCP tools.
Example tool registry:
web.search
filesystem.read
git.clone
database.query
The LLM decides to use web.search.
Example MCP request:
tool: web.search
input:
query: "latest Linux kernel version"
Example response:
{
"version": "6.9",
"release_date": "2024-05"
}
The agent sends this back to the LLM to generate a human-readable answer.
A critical detail: MCP is not implemented directly inside LLMs.
Instead, the agent runtime implements MCP, while the LLM must support structured tool calling.
Therefore, only models capable of function calling / tool use can effectively work with MCP.
Typical requirements:
Without these capabilities, the model cannot reliably interact with MCP servers.
Anthropic created MCP and Claude is the primary reference implementation.
Used in:
GPT-4 and GPT-4o can use MCP through agent runtimes and adapters.
Example frameworks:
These models support structured tool calls required by MCP. (LangChain Docs)
Gemini can interact with MCP through bridge SDKs and agent frameworks. (DaveAI)
Local models can also use MCP if the framework translates tool calls correctly.
Typical setups:
The Qwen 3.5 series (Alibaba) is increasingly used in autonomous agents because of its strong tool-calling and reasoning capabilities.
Relevant properties:
These capabilities make Qwen-3.5 suitable for MCP-based agents when used with frameworks like:
Typical deployments:
Agent runtime
|
| MCP client
|
Qwen-3.5 model
|
MCP servers (tools)
Because MCP itself is model-agnostic, any LLM capable of reliable structured tool calls can be integrated.
Large agent systems often run multiple MCP servers simultaneously.
Examples of MCP servers:
Agents automatically merge all available tools into one registry.
MCP introduces a standardized interface between LLM agents and external tools.
Key characteristics:
However, only LLMs capable of structured tool calling can reliably use MCP.
Examples include:
With MCP, agent frameworks can remain lightweight while gaining powerful capabilities through tool servers.