Model Context Protocol (MCP): Components, Capabilities, and Challenges

What is Model Context Protocol (MCP)? 

The Model Context Protocol (MCP) is an open-source standard created by Anthropic that allows AI models to securely connect to external data sources and tools. It standardizes how large language models (LLMs) interact with file systems, databases, and APIs to retrieve real-time context.

MCP defines a common way for AI assistants, agents, and development environments to discover available capabilities, retrieve context, and request actions from connected systems. Instead of building a custom integration between every AI model and every application, an MCP host, such as an AI assistant or agent platform, connects through MCP clients to MCP servers. These servers expose capabilities such as tools, resources, and prompts.

How MCP works:

• MCP host: The AI application users interact with. It receives requests, coordinates the model, manages permissions, and decides when to use external capabilities.
• MCP client: The communication layer that connects the host to an MCP server. It discovers available capabilities and exchanges standardized protocol messages.
• MCP server: The integration endpoint that exposes external tools, resources, and prompts while handling authentication, execution, and access control.

Core capabilities of MCP:

• Resources: Read external data (like documents, logs, or file systems) to give the AI instant context.
• Tools: Execute specific functions or actions in external systems (like searching the web, updating a CRM, or running code).
• Prompts: Provide the AI with pre-defined templates, instructions, and workflows for specific tasks.

Why MCP Matters

Model Context Protocol (MCP) addresses one of the biggest challenges in enterprise AI: connecting AI applications to external systems in a consistent, secure, and scalable way. 

Instead of requiring custom integrations for every model and application, MCP provides a standardized framework that simplifies tool integration, enables reliable AI actions, and gives organizations greater control over how AI systems access data and services.

• Standardizes AI integrations: MCP replaces one-off integrations with a common interface for exposing tools and data. This reduces duplicated development effort, improves portability across AI applications, and makes integrations easier to maintain and govern.
• Enables structured AI actions: MCP allows AI agents to interact with external systems through well-defined tools and schemas. By exposing available capabilities, required inputs, and expected outputs, it makes tool use more predictable and reliable than ad hoc API integrations.
• Improves enterprise governance: MCP provides a centralized way to control AI access to tools and data. Organizations can enforce permissions, separate read-only and write-capable tools, require approval for sensitive actions, and monitor tool usage through consistent governance policies.

How Model Context Protocol Works: Key Components 

Let’s review the key components of the MCP architecture.

MCP Host

The MCP host is the AI application or environment the user interacts with directly. This could be an AI assistant, coding tool, desktop application, chat interface, or agent platform. The host manages the experience: it receives the user’s request, provides relevant context to the language model, presents responses to the user, and coordinates when external capabilities are needed.

In an MCP architecture, the host controls how the model interacts with external systems. It may decide which MCP servers are available, when a user should approve an action, and how results from external tools are displayed. For example, if a user asks an AI coding assistant to check a repository, the host may determine that a file system or Git server is needed and route that request through the appropriate MCP client.

The host also enforces user permissions, limits which servers are enabled, separates trusted and untrusted capabilities, and provides visibility into tool use. This makes the host the central control point for the AI experience while MCP servers handle the integrations.

MCP Client

The MCP client is the communication layer between the host and an MCP server. Each client typically maintains a dedicated connection to one MCP server, manages the session, and translates requests from the host into MCP-compliant protocol messages. This keeps the host from needing to understand the internal details of each external system.

The client discovers what the server provides. It can request the list of available tools, resources, and prompts, then make those capabilities visible to the host and model. When the model selects a tool or asks for a resource, the client sends a structured request to the server and receives the result in a standardized format.

This separation makes MCP integrations modular. A host can connect to many servers through separate clients, with each client managing one connection. If one server is added, removed, updated, or restricted, the rest of the system can continue operating without redesigning the host.

MCP Server

The MCP server exposes external capabilities to the AI system. It connects to a specific data source, application, API, file system, database, developer tool, or business workflow and makes selected capabilities available through the MCP protocol. Instead of giving an AI model unrestricted access to a system, the server defines a controlled set of actions and context that the model can request.

An MCP server can expose three main types of capabilities. Resources provide readable context, such as documents, records, logs, files, or database entries. Tools allow the AI system to perform actions, such as running a query, creating a ticket, calling an API, checking a deployment, or retrieving live data. Prompts provide reusable templates or workflows that guide the model through a specific task.

The server contains the integration-specific logic. It handles authentication to the underlying system, validates inputs, executes requested operations, and returns structured results. In enterprise environments, MCP servers should use clear scopes, least-privilege permissions, logging, and safeguards for sensitive actions so that AI agents use external systems safely and predictably.

Related content: Read our guide to agentic AI architecture.

The 3 Core MCP Capabilities 

1. MCP Tools

MCP tools are executable functions that an AI model or agent can invoke through an MCP server. They allow the AI system to take structured actions in external systems, such as querying a database, searching a code repository, creating a ticket, calling an API, checking infrastructure status, retrieving logs, or triggering an internal workflow.

Each tool is described with a name, description, and input schema. This helps the AI application understand what the tool does, what information it needs, and how to call it correctly. For example, a tool might require a customer ID, search query, file path, or time range before it can execute.

Tools turn AI systems from passive assistants into systems that perform operational tasks. Sensitive tools, such as those that modify records, send messages, deploy code, or access confidential data, should require appropriate permissions, logging, and human approval when necessary.

2. MCP Resources

MCP resources are readable sources of context exposed by an MCP server. They allow an AI application to access information from files, databases, APIs, documents, logs, application state, knowledge bases, or other external systems without embedding all that data into the model.

Resources are used when the AI system needs more context to answer a question or complete a task. For example, an MCP server might expose a project README, configuration file, customer documentation, security logs, product data, or a database record as a resource. The AI application can retrieve the relevant information and use it as context for reasoning or response generation.

Resources keep information external, current, and selectively accessible. Instead of training or fine-tuning a model on every piece of data, organizations can expose approved resources that the AI system reads when needed. This supports accurate responses while keeping access governed by permissions and policy.

3. MCP Prompts

MCP prompts are reusable prompt templates or guided workflows exposed by an MCP server. They standardize how users and AI systems perform tasks such as summarizing a document, reviewing code, analyzing an incident, generating a report, or following a troubleshooting process.

A prompt can include predefined instructions, required inputs, formatting rules, or task-specific context. For example, a security operations server might provide a prompt for investigating an alert, while a developer tools server might provide a prompt for generating a pull request summary. The user or host can fill in the required variables, and the AI model follows the structured prompt.

Prompts make workflows more consistent and repeatable. Instead of relying on users to write detailed instructions each time, MCP servers can expose approved prompts that reflect best practices, business rules, or team-specific processes. This improves output quality and reduces variation across users and tasks.

MCP vs. APIs vs. Function Calling vs. RAG vs. A2A 

The following table summarizes the difference between MCP and these related concepts. We explore the differences in more detail below.

Aspect	APIs	Function Calling	RAG	A2A
Primary purpose	Connect software systems	Let an LLM invoke predefined functions	Retrieve external knowledge	Coordinate multiple AI agents
Scope	General software integration	Single application or model	Information retrieval	Agent-to-agent collaboration
What MCP adds	Standardizes AI access to APIs as tools and resources	Standardizes discovery and use of tools across systems	Supports retrieval plus actions and prompts	Connects agents to external tools and data
Relationship to MCP	MCP can expose API capabilities	Function calling can invoke MCP tools	RAG can use MCP resources	A2A and MCP are complementary

MCP vs. APIs

APIs are interfaces that allow software systems to communicate with each other. They define endpoints, methods, authentication requirements, request formats, and response structures for interacting with an application or service. Developers use APIs to build integrations between systems, such as connecting a web application to a payment processor, database, CRM, or cloud service.

MCP is designed for AI applications and agents. Instead of requiring each AI system to understand every API directly, MCP provides a standardized layer that describes tools, resources, and prompts in a way AI clients can discover and use. An MCP server may still call traditional APIs behind the scenes, but it packages those capabilities in a format that AI hosts and agents can use consistently.

APIs are the building blocks for software integration, while MCP is a protocol for exposing those capabilities to AI systems in a consistent way.

MCP vs. Function Calling

Function calling allows a language model to produce structured calls to predefined functions. The model can choose a function, fill in the required arguments, and return a structured request that the application executes. This is useful for tasks such as retrieving data, running calculations, calling services, or triggering workflows.

MCP uses similar ideas but operates at a broader integration level. Function calling is usually implemented inside a specific model provider, application, or framework. MCP defines a standard protocol for how AI applications discover and interact with external tools across different servers and systems. It manages a reusable ecosystem of tools, resources, prompts, and server capabilities.

Function calling helps a model take structured actions within an application. MCP helps AI applications connect to many external capabilities through a consistent client-server architecture.

MCP vs. RAG

Retrieval-augmented generation, or RAG, improves AI responses by retrieving relevant information from external sources and adding it to the model’s context. It is commonly used with search indexes, vector databases, knowledge bases, documents, and internal content repositories. The main goal of RAG is to give the model better context so it can generate accurate and grounded answers.

MCP is broader than RAG because it is not limited to retrieval. MCP can expose readable resources that support RAG-like workflows, and it can also expose tools that perform actions and prompts that guide repeatable tasks. For example, an MCP server might let an AI assistant retrieve a document, query a live database, create a support ticket, inspect a codebase, or trigger a workflow.

RAG focuses on bringing external knowledge into the model’s context. MCP connects AI systems to external capabilities, including both information access and action execution.

MCP vs. A2A

Agent-to-agent communication, often shortened to A2A, refers to patterns or protocols that allow multiple AI agents to communicate, coordinate, delegate tasks, or work together. In an A2A model, one agent might ask another specialized agent to perform research, review code, analyze data, or complete part of a larger workflow.

MCP connects AI applications or agents to external tools, resources, and systems. It defines how an AI client communicates with MCP servers that expose capabilities such as database access, file search, API actions, prompts, or business workflows. The server may represent a software system, a data source, or another agent, but MCP focuses on tool and context access.

A2A focuses on communication between agents, while MCP focuses on communication between AI systems and external capabilities. The two can complement each other: agents may use A2A to coordinate and MCP to access the tools and data needed to complete tasks.

Common MCP Use Cases and Examples 

Developer and Coding Assistants

MCP is used to connect AI coding assistants to developer tools, repositories, documentation, issue trackers, CI/CD systems, and local development environments. Instead of relying only on code pasted into a chat window, an AI assistant can use MCP servers to inspect project files, search documentation, review pull requests, query build results, or retrieve engineering context.

For example: A coding assistant could use an MCP server to read a repository structure, summarize recent commits, identify related files, or help debug an error using logs from a development environment. It could also connect to Git platforms, databases, test systems, or package managers, depending on what capabilities the MCP servers expose.

This allows AI coding assistants to work with live project context. Developers can ask higher-level questions, such as why a test is failing, where a function is used, or how to implement a change across multiple files, while the assistant retrieves the context it needs through approved MCP connections.

Enterprise Knowledge Assistants

Enterprise knowledge assistants use MCP to access internal documents, knowledge bases, wikis, policies, tickets, CRM records, support articles, and other business information. Instead of depending only on static training data, the assistant can retrieve current information from approved enterprise systems when answering user questions.

For example: An employee could ask about a company policy, product process, customer account, sales enablement document, or internal procedure. An MCP server could expose relevant documents or records as resources, allowing the assistant to provide an answer based on the organization’s current information.

This is valuable for large organizations where knowledge is spread across many systems. MCP can help unify access to that information while enforcing permissions, auditability, and system boundaries. The assistant becomes a more reliable interface for enterprise knowledge without giving the model unrestricted access to internal sources.

IT and DevOps Automation

MCP can support IT and DevOps automation by connecting AI agents to infrastructure tools, cloud platforms, monitoring systems, ticketing platforms, configuration databases, and deployment pipelines. This allows an AI assistant or agent to investigate issues, retrieve system status, check logs, summarize incidents, or initiate approved workflows.

For example: A DevOps agent could use MCP tools to query application logs, inspect deployment status, check service health, review recent infrastructure changes, or create an incident ticket. In more advanced workflows, it could recommend remediation steps or trigger predefined runbooks, such as restarting a service, scaling a resource, or rolling back a deployment, when appropriate controls are in place.

MCP is useful in these environments because operational work requires access to many systems. By exposing those systems through standardized MCP servers, teams can give AI assistants structured, governed access to the tools they need while keeping sensitive actions controlled and auditable.

Security Operations

Security teams can use MCP to connect AI assistants and agents to security tools, logs, alerts, threat intelligence, case management platforms, identity systems, and incident response workflows. This can help analysts investigate alerts, enrich findings with relevant context, and coordinate response actions across systems.

For example: A security operations assistant could use MCP to retrieve endpoint telemetry, search SIEM alerts, look up indicators of compromise, summarize user activity, correlate events across systems, or create an incident report. It could also help triage alerts by gathering context from identity, network, cloud, and endpoint tools before presenting a recommended next step to the analyst.

MCP can be valuable in security operations because analysts often face fragmented tools and high alert volumes. With carefully designed MCP servers, AI systems can help collect evidence, standardize investigations, and support faster response while still requiring human approval for sensitive actions such as disabling accounts, isolating hosts, or blocking traffic.

MCP Challenges and Limitations

Security and Trust

Giving AI systems direct access to external tools and data also expands the attack surface. Poorly secured MCP servers can expose sensitive information, execute unintended actions, or become targets for prompt injection and other attacks. Organizations must treat MCP servers as trusted integration points rather than simple connectors.

How to overcome:

• Validate MCP servers before allowing them into production.
• Apply least-privilege access so servers expose only the capabilities they need.
• Require authentication and authorization for every tool and resource.
• Monitor and audit tool usage to detect unusual behavior.
• Filter and validate external content before adding it to the model’s context to reduce prompt injection risks.

Governance Complexity

As the number of MCP servers grows, managing them becomes increasingly difficult. Different teams may create servers with different permissions, authentication methods, and security practices, making it harder to understand what AI applications can access and control.

How to overcome:

• Maintain a central registry of approved MCP servers.
• Classify tools by risk level and apply appropriate controls.
• Standardize authentication and permission models across servers.
• Perform regular access reviews and remove unused or outdated servers.
• Enable audit logging to track server usage and administrative changes.

Operational Complexity

Although MCP standardizes AI integrations, it does not eliminate the operational work required to run them. MCP servers must be deployed, monitored, updated, and maintained like any other production service. Reliability issues in a server can directly affect AI applications that depend on it.

How to overcome:

• Treat MCP servers as production infrastructure with defined ownership and support processes.
• Implement monitoring and health checks to detect failures early.
• Test servers regularly for performance, compatibility, and security.
• Document server capabilities and dependencies to simplify troubleshooting.
• Use versioning and change management to prevent breaking existing AI integrations.

Best Practices for Implementing MCP 

1. Design Tools with Clear Boundaries

MCP tools should be designed around specific, well-defined actions rather than broad capabilities. A narrowly scoped tool is easier to understand, test, secure, and audit. For example, a tool that retrieves the status of a deployment is clearer than a tool that runs arbitrary infrastructure commands.

Clear boundaries help the AI model use tools predictably. Each tool should have a clear purpose, expected inputs, expected outputs, and defined failure conditions. Tool descriptions should explain what the tool can and cannot do so the model does not misuse it or select it for the wrong task. Avoid creating tools that combine too many actions, such as reading data, modifying records, and triggering workflows in a single call.

Organizations should separate tools by risk level. Read-only tools, such as search or lookup functions, can be more broadly available. Write-capable tools, such as those that update systems, send messages, create tickets, deploy code, or change configurations, should be tightly restricted. This separation makes it easier to apply different approval, logging, and access control policies based on tool sensitivity.

2. Define Strict Input Schemas

Every MCP tool should use a strict input schema that defines what parameters are accepted, which fields are required, and what values are valid. This reduces ambiguity and helps prevent malformed or unsafe requests from reaching the underlying system. A strong schema also makes it easier for the AI application to understand how the tool should be called.

Schemas should specify data types, required fields, enumerated values, length limits, accepted formats, and safe defaults. For example, a tool that searches logs might require a time range, environment, severity level, and query string. A tool that creates a ticket might require a title, description, priority, and system owner. The more precise the schema, the less room there is for incorrect tool use.

Free-text inputs should be handled carefully because they can introduce ambiguity or injection risk. When free text is necessary, tools should validate and constrain how that text is used, especially if it is passed into databases, shell commands, APIs, search systems, or automation workflows. Input validation should happen on the server side, not only in the model or client, because the MCP server is the final enforcement point before an action is executed.

3. Implement Least-Privilege Access

MCP servers should have only the minimum permissions required to perform their tasks. A server that reads documentation should not have write access to production systems. A server that checks ticket status should not be able to delete tickets, modify workflows, or change user permissions. Limiting permissions reduces the potential damage from mistakes, misuse, compromised credentials, or prompt-injection attempts.

Least privilege should be applied at multiple levels. Server permissions should be limited to required systems. Tool permissions should be limited to specific actions. User permissions should reflect the identity and role of the person using the AI application. Environment permissions should separate development, staging, and production access so that an AI-assisted workflow cannot affect critical systems.

Organizations should review MCP permissions regularly. As tools evolve, permissions can expand beyond their original purpose. Access reviews, credential rotation, audit logs, and policy checks help ensure MCP servers remain aligned with their intended scope. When possible, use short-lived credentials, user-aware authorization, and role-based controls rather than shared, long-lived service accounts with broad access.

4. Require Human Approval for Sensitive Tools

Sensitive MCP tools should require human approval before execution. This is important for actions that modify data, send messages, deploy code, change infrastructure, disable accounts, access regulated information, or affect customers and business operations. Human approval creates a checkpoint between AI-generated intent and execution.

The approval experience should be clear and specific. Before a sensitive tool runs, the user or reviewer should see what action will be performed, which system will be affected, what inputs will be used, and the expected result. For example, an approval prompt should not simply say “run deployment tool.” It should show the application, environment, version, target system, and rollback option where relevant.

Approval requirements should be based on risk. Low-risk read-only actions may not need confirmation every time, while high-impact actions should require explicit approval, stronger authentication, or additional review. Organizations can also use policy-based approvals, where certain actions are automatically blocked, certain actions require manager or administrator approval, and routine low-risk actions are allowed within predefined limits.

5. Maintain an Approved MCP Server Registry

Organizations should maintain an approved registry of MCP servers that are allowed for enterprise use. The registry should include details such as server owner, business purpose, exposed tools, supported resources, authentication method, permission scope, data sensitivity, version, hosting model, and approval status. This gives security, IT, and AI governance teams visibility into which MCP servers exist and what they can do.

A registry helps prevent unmanaged MCP server sprawl. Without a central inventory, teams may create or install MCP servers that duplicate functionality, expose sensitive systems, use weak authentication, or fall outside security review. A registry allows organizations to evaluate servers before adoption, track who is responsible for maintaining them, and remove outdated or risky servers from use.

The registry should support lifecycle management. MCP servers should be reviewed when their tools change, when permissions expand, when new data sources are added, or when ownership changes. Deprecated servers should be retired, vulnerable versions should be updated, and high-risk servers should be monitored closely. Over time, the registry becomes a control plane for governing AI-to-tool access across the enterprise.“`

Governing Agentic AI in Security with Exabeam

As organizations deploy a digital workforce of AI agents, securing their access to critical data is paramount. Exabeam addresses this challenge by providing a secure implementation of the Model Context Protocol, offering a structured gateway that balances innovation with enterprise-grade governance.

How Exabeam applies the Model Context Protocol:

• Secure enterprise data gateway: Exabeam MCP Server acts as a managed gatekeeper that allows external AI agents to consume contextual security telemetry from the platform without direct, unmonitored API integrations
• Developer enablement: Exabeam MCP Server for Developers allows AI assistants like Claude, ChatGPT, and Gemini to interact directly with the Exabeam API surface to browse endpoints, retrieve parameter schemas, and generate custom integration code
• Least-privilege security: Ensures that external AI agents only access the specific resources and tools they need, protecting high-value security data from rogue agent behavior and prompt injection attacks
• Enriched behavioral telemetry: Feeds agent activity logs back into the Exabeam Common Information Model (CIM) to detect anomalies, query abuse, or lateral movement from non-human actors
• Outcomes Navigator strategic alignment: Helps security teams evaluate their detection readiness against agentic threat vectors, aligning coverage with MITRE ATT&CK TTPs and the OWASP Top 10 for LLMs and AI Agents

Learn more and get started by visiting the Exabeam MCP Server for Developers documentation.