What Is MCP (Model Context Protocol) and Why It Matters for Enterprise AI

A new standard is reshaping how AI agents connect to enterprise systems. The Model Context Protocol, created by Anthropic and now backed by OpenAI, Microsoft, and Google, provides a universal interface for AI agents to access tools, databases, and business applications. After years of custom integrations for every AI-to-system connection, MCP offers a standardized approach that’s gaining rapid enterprise adoption.

Understanding MCP matters because it signals a shift in how organizations will deploy AI agents. The protocol addresses a fundamental bottleneck: connecting intelligent models to the data they need to be useful. But MCP also introduces new considerations around security, governance, and how it fits with existing integration infrastructure.

This isn’t just another technical standard. Gartner predicts that 40% of enterprise applications will include AI agents by the end of 2026, up from less than 5% today. MCP is becoming the foundation for how those agents operate.

What MCP actually is

The Model Context Protocol is an open standard that defines how AI applications discover, connect to, and interact with external tools and data sources. Released by Anthropic in November 2024, MCP provides a consistent interface so that AI agents can access different systems without requiring custom code for each connection.

The architecture follows a client-server model. MCP clients run within AI applications like Claude, ChatGPT, or enterprise AI platforms. MCP servers expose specific capabilities: tools that can execute actions, resources that provide data, and prompts that offer reusable templates. The protocol handles communication between them in a standardized way.

The four primary capabilities MCP provides:

Capability	What It Does	Example
Tools	Executable functions for actions	Query a database, create a ticket
Resources	Read-only data and context	Access file contents, retrieve records
Prompts	Reusable prompt templates	Standardized analysis formats
Sampling	Server-requested LLM completions	Agent asks model for clarification

Before MCP, connecting an AI agent to a business system meant building custom integration code. Each combination of AI model and external system required its own implementation. MCP replaces this with a standard interface: build an MCP server once, and any MCP-compatible AI client can use it.

The ecosystem has grown rapidly. Over 5,500 MCP servers now exist on registries like PulseMCP, covering everything from developer tools to business applications. The most popular servers connect AI agents to platforms like GitHub, Figma, and Playwright for browser automation.

The problem MCP was created to solve

Enterprise AI deployments faced what’s called the “N times M problem.” If you have N different AI models that need to connect with M different business systems, you theoretically need N times M custom integrations. Five AI platforms connecting to twenty enterprise tools means one hundred integration projects.

This made enterprise AI adoption expensive and slow. Each new AI tool required rebuilding connections to existing systems. Each new business application required updating every AI integration. Technical debt accumulated faster than value. Organizations familiar with enterprise integration challenges recognize this pattern from traditional system connectivity.

The integration bottleneck had real consequences:

Organizations couldn’t scale AI beyond pilot projects because the integration work overwhelmed available engineering resources. Data remained trapped behind fragmented integrations that couldn’t keep pace with AI deployment ambitions.

Employees created their own AI solutions, what some organizations call “shadow AI,” because official channels couldn’t deliver integrations fast enough. These ungoverned implementations created compliance and security risks.

AI agents operated in isolation rather than maintaining context across the systems relevant to their work. An agent helping with customer service couldn’t access the full picture if relevant data lived in multiple platforms.

MCP addresses this by standardizing the connection layer. Build an MCP server for Salesforce once, and every MCP-compatible AI can use it. The N times M problem becomes N plus M: one server per system, usable by all compatible clients.

Who’s adopting MCP and why it matters

The speed of MCP adoption signals its strategic importance. Within a year of release, the protocol gained backing from the major AI platform providers and significant enterprise software vendors.

Platform adoption:

OpenAI announced MCP support for ChatGPT in December 2025, describing MCP as the foundation for their connector strategy. Microsoft integrated MCP into Visual Studio Code and Visual Studio, enabling GitHub Copilot extensions through the protocol. Google launched fully-managed MCP servers through Google Cloud with enterprise security features.

In December 2025, Anthropic donated MCP to the newly formed Agentic AI Foundation under the Linux Foundation. The foundation’s co-founders include Anthropic, Block, and OpenAI, with support from Google, Microsoft, AWS, Cloudflare, and Bloomberg. This governance move signals that MCP is intended as industry infrastructure, not a proprietary advantage.

Enterprise software vendors building MCP servers:

Category	Vendors
Business intelligence	Tableau, ThoughtSpot, Sisense, GoodData, SAS
Data infrastructure	Snowflake, Databricks, Oracle, Teradata, Confluent
Development platforms	GitHub, Replit, Cursor, Sourcegraph, Codeium

Early enterprise results provide validation. Block, the company behind Square and Cash App, built an internal AI agent called Goose that uses MCP to connect across GitHub, Jira, Snowflake, and internal systems. Thousands of employees use it daily, with reported time savings of 50-75% on common tasks. Bloomberg adopted MCP organization-wide and reported reducing time-to-production from days to minutes for new AI integrations.

How MCP differs from traditional integration

MCP isn’t just another API standard. It reflects fundamentally different assumptions about how software will interact with external systems.

Traditional APIs assume human-written applications making predictable, coded requests. A developer writes specific calls to specific endpoints. The application follows predetermined paths. The system knows what requests to expect.

MCP assumes autonomous AI agents making contextual decisions. The agent reasons about what information it needs and what actions to take. Requests emerge from natural language instructions rather than hardcoded logic. The system must handle unpredictable sequences that evolve based on context.

Aspect	Traditional APIs	MCP
Request model	Discrete, predictable transactions	Orchestrated sequences with evolving context
Decision-making	Hardcoded by developers	Autonomous agent decisions
State management	Stateless request-response	Persistent context across interactions
Discoverability	Documentation for humans	Machine-readable capability descriptions
Reusability	Tightly coupled to applications	Build once, use with any compatible client

This difference has practical implications. Traditional API integrations require developers to anticipate every interaction pattern. MCP enables AI agents to discover available capabilities and decide how to use them based on the task at hand.

MCP also handles “tool overload” differently. An agent with access to hundreds of tools through traditional methods would need to load information about all of them, consuming context window capacity. MCP supports progressive discovery, where agents query for relevant tool categories rather than loading everything upfront. This approach has demonstrated 98.7% reduction in token usage compared to loading all tools simultaneously.

Enterprise considerations and gaps

MCP solves the connection standardization problem but introduces new considerations that enterprises must address.

Security remains a primary concern. Research indicates that 25% of MCP servers have no authentication, and 50% of MCP builders cite security and access control as their primary concern. The protocol enables AI agents to take real-world actions on enterprise systems, making security failures more consequential than traditional API vulnerabilities.

Specific security challenges include tool poisoning, where attackers embed malicious instructions in MCP tool metadata, and prompt injection attacks that could trigger unintended actions across connected systems. Because agents make autonomous decisions, the attack surface expands beyond what traditional API security models address. This is particularly relevant for ServiceNow integrations and other enterprise platforms handling sensitive data.

Governance infrastructure is immature. Security tooling for MCP hasn’t caught up with traditional API management capabilities. Visibility and observability gaps mean agent actions can appear as normal user activity in logs, complicating audit and compliance requirements.

Performance characteristics differ from expectations. MCP introduces baseline latency of 300-800 milliseconds end-to-end, making it unsuitable for real-time applications like trading systems or checkout flows. The protocol uses polling rather than event subscriptions, limiting use cases that depend on immediate notifications.

MCP connects but doesn’t synchronize. The protocol enables AI agents to access data from systems, but it doesn’t keep that data consistent across systems. If customer information differs between your CRM and support platform, MCP doesn’t resolve that inconsistency. The agent accesses whatever data exists in each system, conflicts included.

Consideration	Status
Security standards	Emerging, significant gaps
Enterprise governance	Immature tooling
Real-time performance	Not suitable (300-800ms latency)
Data synchronization	Not addressed by protocol
Cross-platform consistency	Requires separate infrastructure

This last point matters for enterprise deployments. MCP provides the pipes for AI agents to reach your systems. It doesn’t provide the plumbing that keeps those systems aligned with each other.

MCP and your existing integration infrastructure

Organizations evaluating MCP often ask whether it replaces their existing integration platforms. The answer reveals how different problems require different solutions.

MCP standardizes how AI agents connect to systems. Integration platforms standardize how data flows between systems. These are complementary functions, not competing ones.

Consider what each layer provides:

Integration platforms maintain ongoing data synchronization. When a customer record updates in Salesforce, it should reflect in your support platform and marketing tools. This requires persistent infrastructure that monitors changes, handles conflicts, and ensures consistency. Integration platforms like two-way sync solutions provide this foundation.

MCP enables AI agents to access that synchronized data through a standard interface. The agent queries the CRM through an MCP server and gets current information because the integration layer kept that information current. Without the integration layer, the agent might access stale or conflicting data across systems.

The relationship is layered:

Integration infrastructure keeps your business systems synchronized. MCP servers expose those synchronized systems to AI agents. The agent benefits from both: standardized access through MCP, and consistent data through integration.

Organizations that invested in integration infrastructure find that MCP extends the value of that investment. AI agents can now access the synchronized data that integration provides. Organizations without solid integration foundations discover that MCP alone doesn’t solve the data consistency problems their agents encounter.

For teams already using two-way sync between work management platforms, MCP represents an additional access layer for AI agents rather than a replacement for existing integration.

What comes next

MCP’s trajectory suggests it will become standard infrastructure for enterprise AI. The governance transfer to the Linux Foundation, backing from all major AI platforms, and rapid ecosystem growth indicate sustained momentum rather than a passing trend.

Near-term developments to watch:

Security and governance tooling will mature as enterprise adoption increases. The current gaps create risk that major vendors are actively working to address. Expect enterprise-grade security features to emerge as table stakes.

Multi-agent orchestration will become more common. Rather than single agents handling requests, specialized agents will coordinate through MCP, each accessing the systems relevant to their function. This “agent squad” pattern expands what’s possible but also expands complexity.

The line between MCP servers and integration platforms may blur. Some integration vendors will expose their capabilities through MCP servers. Some MCP implementations will add synchronization features. The current clear distinction may become a spectrum.

For enterprise planning:

Evaluate MCP readiness of your critical systems. Which vendors offer MCP servers? What capabilities do they expose? Understanding the current landscape helps prioritize where AI agents can operate effectively.

Assess your data foundation. AI agents accessing systems through MCP are only as useful as the data in those systems. If your platforms contain conflicting or stale information, agents will work with that flawed data. Integration infrastructure that maintains consistency becomes more valuable, not less, as MCP adoption increases.

Consider security implications before deployment. The 25% of MCP servers without authentication represents significant risk. Ensure your organization’s security requirements are met before exposing systems to AI agents.

MCP: The protocol for getting more out of every agent

MCP represents a genuine shift in how AI agents will access enterprise systems. Understanding both its capabilities and its limitations helps organizations adopt it effectively while maintaining the data foundations that make AI agents actually useful.

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