MCP: The protocol that changed how AI integrates with enterprise systems
ARTIFICIAL INTELLIGENCE.
AI agents promise to revolutionize business operations through task automation, insight generation, and increasingly sophisticated customer interaction management. However, reliably and efficiently connecting agents to real-time information has represented a significant obstacle. This complexity limits the scope and effectiveness of AI implementations.
To address the complexities and fragmentation in how AI models interact with various systems and data sources, Anthropic created the Model Context Protocol (MCP). This protocol is often likened to the "USB-C port for AI" due to its emphasis on simple, universal connectivity and interoperability. Just as USB-C standardized physical connections and data transfer for a wide range of devices, MCP aims to provide a unified framework for AI models to receive and process context from diverse inputs, streamlining their integration into applications and workflows. This approach significantly reduces the overhead associated with custom integrations, fostering a more efficient and scalable AI ecosystem.
MCP enables AI models to interact with external tools like Google Calendar or Slack.
What is the MCP protocol
Model Context Protocol is an open standard that enables AI applications to dynamically connect with external enterprise systems.
Unlike traditional integrations that require specific code for each connection, MCP provides a “universal language” that AI systems can interpret at runtime.
The protocol functions as an automatic translator between AI agents and data sources. It allows systems to identify available actions (Tools) and access necessary information (Resources) on demand.
Anthropic has demonstrated its viability through the development of servers, tools, and SDKs that align with the protocol's principles. With backing from companies like OpenAI, Replit, and a growing open source ecosystem, MCP is gaining traction in the enterprise market.
The protocol is not exclusive technology. It's a standard that developers worldwide are adopting and implementing, similar to how HTTP became the set of principles for web communication.
Note: This article is based on the publication by Felipe Jaramillo Fonnegra, CEO of Aplyca in VKTR media in April, 2025. It has been expanded with recent information on MCP adoption as well as specific use cases.
Where MCP fits in the enterprise
MCP unlocks smarter and contextually aware AI agents by connecting them with unique enterprise data in real time.
The protocol transcends the generic knowledge of artificial intelligence models, providing specific operational insights based on current company information.
A fundamental benefit is the ability to integrate multiple data sources without traditional development cycles. Systems like CRM (Salesforce, HubSpot), ERP (SAP, Oracle), marketing analytics platforms, and support tools can connect without technical friction.
While major software providers announce “agentic” capabilities, most focus on automating repetitive tasks. Enabling agents to interact with enterprise data in real time presents immense opportunities and significant challenges. Adding context in a controlled and secure manner across different AI platforms makes a profound difference in implementation effectiveness.
Use cases range from accelerating internal development workflows (integrating Slack, Jira, Figma) to driving sophisticated customer-facing solutions.
Strategically choosing providers that support or plan to support MCP standards helps future-proof the AI stack. This ensures greater flexibility and avoids long-term vendor lock-in.
How MCP works internally
MCP provides AI applications with a "universal remote control" to interact with external systems. The protocol enables identifying available capabilities and executing actions without prior programming.
Rather than developers rigidly coding integrations during design, the AI system "reads the instructions" from external systems at runtime.
This shift decouples AI from fixed integrations. Organizations can evolve capabilities, connect new tools, or update data sources more quickly, significantly reducing development burden.
Inspiration from established protocols
The Anthropic team drew inspiration from proven protocols like LSP (Lenguaje Server Protocol), used in software development for standardized interaction between editors and tools.
MCP seeks simplicity and extensibility through established formats like JSON RPC. The protocol revives concepts like HATEOAS (Hypermedia as the Engine of Application State) from the REST world, applying them to the artificial intelligence context.
Long-term vision
The MCP ecosystem envisions rich composable AI applications and sophisticated 'agentic' behaviors enabled by bidirectional communication.
Systems will be able to negotiate capabilities, understand data structures, and execute actions autonomously, similar to how web browsers interpret HTML without prior knowledge of each specific site.
The integration bottleneck that MCP solves
AI integration requires developers to meticulously program each specific connection between artificial intelligence and external systems like CRM, ERP, or internal databases.
This method presents critical problems such as:
Fragility and constant maintenance
Changes in external tools frequently require rewriting complete integrations. This generates high maintenance costs and continuously consumes development resources.
Limited implementation speed
The process is slow and hinders the rapid deployment necessary in dynamic enterprise environments. Each new integration can take weeks or months.
The paradigm shift with MCP
MCP transforms this model by allowing AI applications to discover and connect to tools and data dynamically and in real time.
The connection works similarly to how a person navigates by clicking links on a website, without the need for prior programming.
Beyond RAG
After discovering the capabilities of large language models, many teams adopted techniques like RAG (Retrieval-Augmented Generation), which represents content in vector space and retrieves relevant fragments.
RAG does not inherently solve the problem of enabling agents to interact with multiple live data sources or execute actions through tools and APIs.
MCP provides the robust and standardized approach necessary for these dynamic capabilities, especially as part of existing enterprise software solutions.
What to do now to stay competitive in the MCP era
Despite the typical challenges of new standards, MCP is gaining significant traction due to enterprise demand and a growing developer community.
For business leaders, this represents a crucial shift that requires immediate strategic action.
1. Audit your current AI infrastructure
Evaluate existing implementations and identify where integration limitations restrict value. It's necessary to map critical data sources, enterprise tools, and systems that could benefit from dynamic access.
Consider both internal and customer-facing use cases. Document current integration maintenance costs.
2. Launch focused pilot projects
Implement controlled experiments in areas where flexible integration can quickly demonstrate value. Prioritize use cases that currently require significant development or where frequent changes generate high costs.
Establish clear success metrics and realistic timeframes for evaluation.
3. Evaluate vendor commitments
When selecting new vendors or renewing contracts, prioritize those with commitment to interoperability standards.
This reduces the risk of vendor lock-in and positions the organization to leverage future innovations in the enterprise AI ecosystem.
4. Establish internal champions
Identify and empower technical and business leaders to explore implementation opportunities. These champions should understand technical capabilities and strategic business context.
5. Develop technical competencies
Invest in training for engineering teams on modern AI integration protocols. Establish centers of excellence or working groups focused on interoperable architectures.
6. Implement robust data governance
Before enabling dynamic agent access to enterprise systems, ensure security, privacy, and governance controls. MCP amplifies both opportunities and risks related to data management.
List of available MCP servers
The developer community has created a growing ecosystem of MCP servers for popular enterprise systems. These servers act as standardized connectors between AI agents and specific data sources.
Servers for enterprise systems
Salesforce MCP Server: connects AI agents with CRM data, enabling customer queries, opportunity updates, and sales pipeline analysis.
Google Workspace MCP Server: integrates Gmail, Google Drive, Calendar, and Docs for contextual access to enterprise communications and documents.
Slack MCP Server: enables agents to search conversations, obtain channel context, and access distributed organizational knowledge.
PostgreSQL/MySQL MCP Servers: direct connection to relational databases with secure query capabilities.
GitHub MCP Server: access to repositories, issues, pull requests, and technical documentation.
Servers for analytics and data
Google Analytics MCP Server: extraction of traffic metrics, conversions, and user behavior.
Elasticsearch MCP Server: search and analysis of large volumes of indexed data.
AWS S3 MCP Server: access to files and data stored in cloud infrastructure.
Servers for development tools
Jira MCP Server: project, task, and sprint management through AI agents.
Figma MCP Server: access to designs, components, and design assets.
Linear MCP Server: integration with product management and issue systems.
The list continues to expand weekly as more developers create connectors for industry-specific systems.
Where to find MCP servers
MCP servers are primarily available through open source repositories and specialized developer communities.
GitHub and official repositories
Anthropic's official repository contains verified servers maintained by the community. These follow security best practices and are regularly audited.
Organizations and individual developers also publish their own MCP servers on GitHub with open source licenses that allow enterprise use.
Package registries
MCP servers are available in standard package registries like npm (Node.js), PyPI (Python), and other programming language ecosystems. This facilitates installation and dependency management in existing enterprise projects.
Specialized communities
Forums like Anthropic's Discord, Reddit communities dedicated to enterprise AI, and LinkedIn groups bring together developers who share customized servers for specific use cases.
Emerging marketplaces
Specialized marketplaces are emerging where software providers offer certified MCP servers for their platforms, facilitating enterprise adoption with support guarantees.
MCP for ChatGPT
The integration of Model Context Protocol (MCP) with ChatGPT represents a significant opportunity to extend the capabilities of the most popular conversational AI platform.
Connection with enterprise systems
Through MCP, ChatGPT can dynamically connect to internal enterprise systems without requiring custom API integrations for each case.
This enables GPT to access corporate databases, CRM systems, analytics platforms, and productivity tools in real time.
Enterprise use cases with ChatGPT and MCP
Employees can ask questions in natural language and receive answers based on updated enterprise data. For example, querying the status of sales opportunities, generating specific metric reports, or searching for information in internal documentation.
The implementation eliminates the technical barrier between ChatGPT's conversational interface and the data that truly matters to the organization.
On the other hand, enterprise architecture teams have the ability to have integrations and deploy them across multiple AI platforms, allowing operational flexibility and decreasing total costs of artificial intelligence solutions.
Security considerations
When implementing Model Context Protocol (MCP) with ChatGPT, organizations must establish robust access controls. This includes user authentication, granular permissions on what data each employee can access, and auditing of all queries.
MCP allows implementing these security layers centrally, applying consistent policies regardless of which AI agent accesses the systems.
MCP for websites
The application of Model Context Protocol (MCP) in web environments represents a strategic opportunity particularly relevant for CMOs and digital leaders.
1. Advanced contextual personalization
This protocol allows AI agents on websites to quickly access purchase history, browsing preferences, CRM data, and real-time behavior.
This enables personalized recommendations and content that go beyond traditional segmentation.
2. Intelligent conversational assistants
Chatbots and virtual assistants can query multiple systems (inventory, prices, availability, policies) without rigidly coded integrations, which reduces response times and improves the accuracy of information provided to customers.
3. Dynamic content optimization
Content, offers, and layouts can automatically adjust based on performance data, audience segmentation, and business objectives.
AI agents can perform continuous A/B testing and real-time optimization without manual intervention.
4. Customer process automation
From generating complex quotes to resolving service issues, agents can access necessary systems in real time to complete transactions.
For organizations with significant digital presence, MCP eliminates the technical barrier that has separated AI capabilities from the data and systems that make them useful for customers.
MCP for SEO
Model Context Protocol (MCP) revolutionizes search engine optimization strategies by enabling real-time data-based analysis and optimization.
Connection with SEO tools
MCP connects AI agents with keyword research platforms, Google Search Console, Google Analytics, competitive analysis tools, and technical monitoring systems.
This integration allows synthesizing information from multiple sources to generate highly optimized content strategies.
Automatic opportunity identification
Agents can detect content gaps based on high-intent searches not covered by your site or competitors.
They analyze successful ranking patterns and suggest optimizations for metadata, content structure, and internal linking.
Continuous technical optimization
MCP enables continuous monitoring and improvement of technical aspects that impact SEO. Agents analyze Core Web Vitals, load times, crawling errors, and indexing issues.
They can automatically suggest or implement corrections based on updated best practices.
Adaptation to algorithmic changes
When search engines update algorithms or when competitors implement new strategies, agents with MCP can detect these changes and proactively adjust tactics.
Strategic value for marketing
For CMOs, this means scaling SEO capabilities without increasing teams. In Latin American markets where competition for organic visibility is intense, MCP transforms SEO from a reactive discipline to a proactive and automated process that drives measurable business results.
Everyone can use MCP: Democratization of enterprise AI
Model Context Protocol (MCP) represents a fundamental shift in the accessibility of advanced AI capabilities for organizations of all sizes.
An open standard without entry barriers
Unlike proprietary solutions that require expensive licenses or complex enterprise contracts, MCP is an open protocol available to any organization.
Small and medium-sized businesses can leverage the same integration capabilities as global corporations, leveling the competitive playing field.
Collaborative ecosystem
The developer community is creating and sharing MCP servers in an open source manner. This means an organization can benefit from the work of thousands of developers without needing to build everything from scratch.
Knowledge, best practices, and solutions to common problems are openly shared, accelerating adoption.
Reduction of vendor dependence
By standardizing on MCP, organizations reduce their dependence on specific AI vendors. Agents can switch platforms without needing to rewrite complete integrations.
This provides strategic freedom and negotiating power when evaluating technology options.
The time to act is now
As MCP evolves from emerging trend to essential infrastructure, organizations experimenting now develop significant competitive advantages.
The future belongs to companies that can leverage AI agents connected to their exact data and specific tools when needed.
Perspective for Latin America
The Latin American market presents unique characteristics: heterogeneous technology ecosystems, need for integration between legacy and modern systems, and growing demand for sophisticated digital experiences.
Organizations that rapidly adopt MCP will have a significant competitive advantage. The window to experiment and learn is open, but it will close quickly as these capabilities become basic market expectations.
Strategic action today determines tomorrow's competitive positioning. MCP is not just another technology: it's the infrastructure that will define how organizations leverage artificial intelligence in the next decade. That's why it's important to be at the forefront and get advice from expert developers.
Contact us to implement MCP and create AI agents that interact directly with your enterprise platforms.