The Tool Desk
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An agent is a system that pursues a goal by taking in context, choosing actions, and carrying them out through available capabilities, subject to constraints or authority. In an object-oriented analogy, an agent class is a reusable design; an agent instance is a particular agent with its own current task, context, and—if its implementation provides them—state and permissions. The 2026-07-28 Model Context Protocol (MCP) specification does not define “agent” universally. It defines a protocol for connecting LLM applications with external data and tools, so its host, client, and server roles should not be mistaken for agents themselves.
What does “agent” mean here?
There is no single definition of “agent” that applies across every AI product or framework. For clarity, this article uses an operational definition: an agent is a system that pursues a goal by receiving context, selecting actions, and executing them through capabilities it can access, within some constraints or authority.
This definition describes a pattern of behavior, not a particular model, application, protocol, or tool. A system that only returns a response to a prompt may not fit this framing; a system that can decide among actions and use available capabilities to make progress toward a goal does.
The definition is an explanatory choice, not a rule set by MCP. The 2026-07-28 MCP specification describes protocol architecture and features, not a universal ontology of agents.
What are an agent class and an agent instance?
Agent class: the reusable design
In object-oriented programming, a class describes a kind of object: its structure, interfaces, and possible behavior. By analogy, an agent class can describe a reusable agent design, such as the capabilities it may use, how it selects actions, and what constraints apply.
Agent instance: one particular agent
An instance is one particular realization of that design. It may have a specific task, current context, runtime state, and permissions. These are properties of the instance only when the implementation actually gives it those properties; the class/instance analogy does not guarantee persistent memory, a unique identity, or any particular permission model.
For example, a design for an assistant that can search documents might be instantiated twice: one instance handles a question about a project folder, while another handles a different folder under different access rules. They may share a design while having distinct tasks and authority.
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How does MCP fit into the picture?
MCP is an open protocol that connects LLM applications to external data sources and tools. It uses JSON-RPC 2.0 and distinguishes three protocol roles:
| Role | What it does | How it relates to an agent |
|---|---|---|
| Host | The LLM application that initiates connections. | May be part of an agent system, but being a host does not by itself make it an agent. |
| Client | A connector within a host that communicates with a server. | Provides protocol connectivity; it is not automatically an agent. |
| Server | A service that provides context and capabilities. | Can supply capabilities an agent uses, but protocol participation alone does not confer agency. |
An agent system could use a host application and its MCP client to reach one or more MCP servers. The agent describes the system’s goal-directed behavior; MCP describes how components communicate and expose capabilities. Treating “agent,” “host,” “client,” and “server” as interchangeable mixes different layers.
What can MCP provide to an agent?
The specification describes server features including resources, prompts, and tools, and a client feature called elicitation. Resources can provide context, prompts can supply reusable interaction templates, and tools can expose actions. The specification also describes extensions, including Tasks, MCP Apps, and Skills over MCP.
These capabilities do not determine how an application manages an agent’s identity, task, memory, or lifecycle. An application can maintain its own state and decide which capabilities an instance may use; MCP does not automatically provide those application-level choices.
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Does stateless MCP make an agent stateless?
No. The 2026-07-28 specification makes MCP requests stateless and self-contained, with capability negotiation on each request. The maintainers’ release post says the update retires the earlier initialize/initialized exchange and session header. It also describes optional server discovery, header-based routing, cache hints, and other changes.
That is a protocol-level design decision, not a requirement that the application discard its own state. An application can preserve an agent’s task or context across separate MCP calls using its own state-management approach. Changing the communication protocol therefore does not, by definition, mean the same agent has become a different agent—or that an agent must forget what it was doing.
The release post also describes related changes: moving Tasks to an extension, supporting server requests such as elicitation without a continuously open bidirectional stream, and hardening authorization, including a shift away from Dynamic Client Registration toward Client ID Metadata Documents. It says Roots, Sampling, Logging, and legacy HTTP+SSE are deprecated with a minimum twelve-month window. These are version-specific details; implementation guidance and deprecation status can change, so check the current specification and SDK guidance when building against MCP.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does MCP’s security guidance mean for agent actions?
MCP can enable access to data and paths for code execution. Standardizing how a tool is described or called does not make its behavior inherently safe. The specification emphasizes user consent and control, privacy, and careful treatment of tool behavior; it requires explicit user consent before tool invocation.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsIn practice, an agent design should make clear which capabilities an instance can reach and where approval is required. The protocol supplies a way to communicate with tools; the application and its users still need to govern what actions are allowed.
A practical way to distinguish designs and running agents
When evaluating an implementation, separate the reusable design from a particular run, then examine the implementation choices that shape that run:
- State ownership: Which component retains task progress or context between calls?
- Available capabilities: Which tools, resources, or other services can the instance access?
- Permission boundary: Which actions require explicit approval, and who grants it?
- Lifecycle: Is the instance short-lived for one task, or does the application preserve it for longer work?
These are useful design questions, not properties guaranteed by MCP. A protocol-level change such as removing session state does not settle how an application answers them.
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