MCP Architecture and Workflow

• MCP Architecture and Workflow is a standardized open protocol that enables unified interactions between AI models and external tools and services.
• It orchestrates secure, real-time communications among host, client, and server components for efficient tool invocation and data retrieval.
• The protocol emphasizes robust security, lifecycle management, and standardization to mitigate risks like sandbox escape and configuration drift.

The Model Context Protocol (MCP) is a standardized open protocol designed to unify and streamline interactions between AI models and external tools, data sources, and services. By introducing a common interface and lifecycle management framework, MCP enables seamless orchestration across heterogeneous systems, advancing interoperability, security, and workflow consistency for tool-augmented AI and agentic systems. The following sections provide a detailed reference for MCP architecture, workflow, server lifecycle, security concerns, adoption, and future directions, as codified in the primary source (Hou et al., 30 Mar 2025).

1. Architecture and Workflow

MCP defines a tripartite architecture:

• MCP Host: An AI application or computational environment executing tasks and running the integrated MCP client (e.g., Claude Desktop, Cursor AI-assisted IDE).
• MCP Client: An embedded intermediary responsible for analyzing prompts, querying capability catalogs from MCP servers, sending requests, handling workflow progress notifications, and relaying results to the user. The client enforces schema compliance and operates over secure, real-time bidirectional transport.
• MCP Server: The interface to external tools, exposing a catalog of operations, data resources, and reusable prompt templates. The server supports the invocation and orchestration of tools following a supply-consume model.

The end-to-end MCP workflow can be expressed as:

$\text{MCP} = \{ H, C, S \}$

where $H$: Host, $C$: Client, $S$: Server.

Interaction Sequence

1. User Input: A prompt is issued via the host.
2. Client Mediation: The MCP client analyzes the prompt, queries one or more servers, and determines suitable tools/resources.
3. Server Execution: The MCC server executes or orchestrates external APIs, fetches required data, and processes results.
4. Notification and Output: The client receives results and intermediate notifications, relaying output to the user and maintaining real-time synchronization.

The protocol employs a secure transport layer with support for notifications and status updates, maintaining tight synchronization between the AI system and external context.

2. Lifecycle of MCP Server

The MCP server lifecycle is demarcated in three critical phases, each with its own operational and security considerations:

Phase	Core Activities	Principal Security Risks
Creation	Registration, config, code integrity	Name collision, installer spoofing, backdoors
Operation	Active request handling, sandboxing	Tool name conflicts, command overlaps, sandbox escape
Update	Patching, version control, maintenance	Stale privilege, redeployment of vulnerable versions, config drift

Formally, the lifecycle can be described as:

$$S_{\text{lifecycle}} = S_{\text{create}} \cup S_{\text{operate}} \cup S_{\text{update}}$$

or time-parameterized:

$$S(t) = \begin{cases} C & \text{if } t \in [t_0, t_1] \ O & \text{if } t \in [t_1, t_2] \ U & \text{if } t \in [t_2, t_3] \end{cases}$$

Creation Phase: Involves unique naming, installer deployment with config and manifests, and strict code integrity checks to prevent deployment of tampered binaries.

Operation Phase: Encompasses tool invocation within a sandbox. Threats include ambiguous tool naming (leading to unintended or malicious selection), overlapping commands, or sandbox escape through vulnerabilities.

Update Phase: Centers on applying patches, version management, and privilege revocation. Risks emerge from privilege persistence, unintentional rollbacks to insecure versions, or configuration drift leading to divergence from a secure baseline.

3. Security and Privacy Threats

Each lifecycle phase introduces distinct security and privacy risks:

• Creation:
  • Name Collision: Malicious servers mimic trusted names to deceive clients.
  • Installer Spoofing: Modified installers can inject malicious payloads.
  • Code Injection/Backdoor: Unauthorized code insertion at startup can grant persistent covert control.
• Operation:
  • Tool Name/Command Collisions: Overlapping tool names or command schemas risk invoking unintended operations.
  • Sandbox Escape: Host resources become accessible if the sandbox is compromised.
• Update:
  • Privilege Persistence: Inadequate revocation may allow outdated privileges to linger.
  • Insecure Rollback: Accidental redeployment of previous, vulnerable versions enables exploitation.
  • Configuration Drift: Sensitive deviations from the secure configuration baseline increase attack surface.

Mitigation strategies include centralized namespace policies, cryptographic installer/server verification, robust sandbox isolation, automated auditing, comprehensive logging, and rigorous configuration management. Emphasis is placed on enforcing granular permission control in multi-tenant deployments and validating all integrations via secure, community-verified installers.

4. Ecosystem Landscape and Industry Adoption

MCP has transitioned from a niche protocol to an industry-wide standard, with significant adoption and ecosystem development:

• Industry Integration:
  • Anthropic (Claude Desktop), OpenAI (Agent SDK), Baidu Maps, and Cloudflare (multi-tenant deployments) have integrated MCP.
  • Developer platforms (Cursor, JetBrains, Sourcegraph Cody) use MCP for efficient multi-step automation.
• Community Registries:
  • Platforms such as MCP.so, Glama, and PulseMCP collectively maintain thousands of MCP servers, supporting rapid developer publishing and ecosystem evolution.
• SDKs and Tooling:
  • Supported across TypeScript, Python, Java, Kotlin, and C#, with utilities like EasyMCP, FastMCP, and Foxy Contexts further lowering development friction.

This proliferation accelerates cross-platform tool integration but accentuates the challenge of decentralized security, version control, and authentication standards.

5. Standardization, Governance, and Recommendations

The paper proposes several pathways for robust, future-ready MCP development:

• Namespace and Package Management: Establish centralized authorities for tool naming, versioning, and server registration to obviate naming conflicts and collision attacks.
• Cryptographically-Verified Distribution: Direct investments into installer signing and end-to-end verification of server code and manifests.
• Security Automation: Adoption of continuous sandbox monitoring, comprehensive request audit trails, and anomaly detection for command overlaps or privilege persistence.
• Granular Authorization: Development and enforcement of permission models for fine-grained, cross-tenant and remote host access control.
• Community Practices: Adherence to documented integration guidelines, strict version control during updates, use of verified auto-installers, and extensive context-aware agent orchestration.

Stakeholder responsibilities are delineated as follows:

• Maintainers: Centralized registry management and secure installer frameworks.
• Developers: Secure code and automated configuration/version management.
• Researchers: Focus on vulnerability mapping in sandboxing and workflow orchestration.
• End-users: Use of verified servers, periodic updates, and monitoring for privilege drift.

6. Strategic Outlook and Future Directions

The MCP protocol is positioned as a foundational interface for open, secure, and sustainable AI-to-tool integration. Immediate priorities for advancing MCP include:

• Scaling governance mechanisms and centralized registries to keep pace with rapid ecosystem growth.
• Enhancing security oversight through automation and minimizing manual intervention in server lifecycle operations.
• Guaranteeing workflow consistency and resilience across increasingly complex, multi-tool, multi-tenant applications.

The protocol’s long-term sustainability will depend on the successful balancing of decentralized innovation with robust, evolving standards for interoperability, security, and maintainability across the AI tool integration landscape.