What Is MCP? The Protocol Transforming AI Agents and LLM Integration
Apr 2, 2025
6 MIN. READ
Written by Chaz Englander
I’m not planning to become an educator, but people should really pay attention to this MCP (Model Context Protocol) chat—it's mind-bending. LLMs on their own aren't all that useful, which is why agents have exploded in popularity.
Agents are essentially LLMs with access to external tools.The problem with agentic systems that no one talks about is that as you layer on more capabilities, they become incredibly complex to maintain and often fail to work cohesively, leading to unreliable and brittle systems at scale. We should know; we've built arguably one of the most advanced agentic systems in the world.
Right now, each service provider speaks a different language as their APIs use different protocols, structures, request formats, etc. Plus, you have to constantly update each agent to keep up with new developments from each service provider, which is a huge hassle. This is where MCP comes in. MCP acts as a standardized layer between the LLM and external services, translating and simplifying communication.
So, how does the MCP ecosystem work, and who builds MCP? Anthropic (the inventors) set the standard, and it's up to service providers to build and maintain their own MCP integrations. If they don't, it makes their services difficult for LLMs to communicate with. However, some providers might argue they already have APIs and may hesitate to maintain their own MCP servers. This is where the open-source community comes in, developing and maintaining open-source MCP implementations, which is an exciting and powerful aspect of MCP.
Twelve months ago, it seemed almost inevitable that we needed a new protocol, but it was super unclear who would set the initial protocol and how it would become widely adopted, if at all. Two weeks ago, Sam Altman posted on X that "OpenAI people love MCP and we are excited to add support across our products" (which triggered this post). OpenAI enabling their Agents SDK to plug into MCP (alongside ChatGPT) makes it clear that MCP is poised to be the protocol of the future. Why is this so important?
Prior to MCP, if you wanted an LLM to work with an external tool (e.g. send an email, fetch a Slack message, perform a Google search, access a database), you had to build and maintain integrations for each one. Now, because MCP integrations standardize communication, adding new tools and maintaining them becomes infinitely simpler and far more robust. This means that soon you'll be able to interact with your entire digital ecosystem through a single application—retrieving information & performing actions.
For example: "Hey LLM, order the ingredients for a green Thai curry tonight." The LLM could first search (via Perplexity's MCP integration) for the list of ingredients, then place the order and track it for you (via UberEats' MCP integration).
The incredible thing is that once these MCP integrations exist, you could realistically build the above example in a matter of hours from scratch.
To see the full LinkedIn post, click this link.
https://www.linkedin.com/posts/activity-7315709034366402560-2BmB
FAQs
1. What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) enables AI models, such as large language models (LLMs), to securely and efficiently connect with external data sources, tools, and services through a standardized interface.
2. Why was MCP created, and what problem does it solve?
MCP was created to address the complexity and fragmentation of integrating AI models with diverse external systems. Before MCP, each integration required custom code, leading to maintenance challenges and brittle systems. MCP standardizes these connections, making integrations more robust and scalable.
3. What are the core components of the MCP architecture?
MCP uses a client–host–server pattern:
Host: The application the user interacts with (e.g., an IDE or chatbot)
Client: Manages the connection to an MCP server
Server: Exposes tools, resources, and prompts via a standard API to the AI model.
4. What are ‘tools,’ ‘resources,’ and ‘prompts’ in MCP?
Tools: Executable functions (e.g., API calls, database queries) that the LLM can use.
Resources: Data sources or endpoints providing information (like files or logs).
Prompts: Predefined templates guiding how the model interacts with tools and resources.
5. How does MCP improve the reliability and maintainability of agentic AI systems?
By providing a universal protocol, MCP reduces the need for custom, one-off integrations and ensures that updates or changes in external tools can be managed at the server level, rather than requiring changes across every AI agent.
6. How does MCP handle security and privacy?
MCP is designed with a local-first security approach. It requires explicit user approval for access to tools or resources, and servers typically run locally unless remote access is permitted, ensuring sensitive data remains protected.
7. Who builds and maintains MCP integrations?
Service providers are responsible for building and maintaining their own MCP integrations. The open-source community also plays a key role, developing and maintaining open-source MCP implementations for broader adoption.
8. How is MCP being adopted in the AI ecosystem?
MCP has seen rapid adoption, with support from major companies like Anthropic, OpenAI, and AWS. Developer tools and platforms such as Cursor, Replit, and Sourcegraph have integrated MCP, and community-driven connectors are expanding its ecosystem.
9. What are some real-world examples of MCP in action?
With MCP, an LLM can interact with multiple services seamlessly. For example, it can search for recipe ingredients via one MCP integration, place an order through another, and track delivery—all from a single conversational interface.
Citations
Written by Chaz Englander
I’m not planning to become an educator, but people should really pay attention to this MCP (Model Context Protocol) chat—it's mind-bending. LLMs on their own aren't all that useful, which is why agents have exploded in popularity.
Agents are essentially LLMs with access to external tools.The problem with agentic systems that no one talks about is that as you layer on more capabilities, they become incredibly complex to maintain and often fail to work cohesively, leading to unreliable and brittle systems at scale. We should know; we've built arguably one of the most advanced agentic systems in the world.
Right now, each service provider speaks a different language as their APIs use different protocols, structures, request formats, etc. Plus, you have to constantly update each agent to keep up with new developments from each service provider, which is a huge hassle. This is where MCP comes in. MCP acts as a standardized layer between the LLM and external services, translating and simplifying communication.
So, how does the MCP ecosystem work, and who builds MCP? Anthropic (the inventors) set the standard, and it's up to service providers to build and maintain their own MCP integrations. If they don't, it makes their services difficult for LLMs to communicate with. However, some providers might argue they already have APIs and may hesitate to maintain their own MCP servers. This is where the open-source community comes in, developing and maintaining open-source MCP implementations, which is an exciting and powerful aspect of MCP.
Twelve months ago, it seemed almost inevitable that we needed a new protocol, but it was super unclear who would set the initial protocol and how it would become widely adopted, if at all. Two weeks ago, Sam Altman posted on X that "OpenAI people love MCP and we are excited to add support across our products" (which triggered this post). OpenAI enabling their Agents SDK to plug into MCP (alongside ChatGPT) makes it clear that MCP is poised to be the protocol of the future. Why is this so important?
Prior to MCP, if you wanted an LLM to work with an external tool (e.g. send an email, fetch a Slack message, perform a Google search, access a database), you had to build and maintain integrations for each one. Now, because MCP integrations standardize communication, adding new tools and maintaining them becomes infinitely simpler and far more robust. This means that soon you'll be able to interact with your entire digital ecosystem through a single application—retrieving information & performing actions.
For example: "Hey LLM, order the ingredients for a green Thai curry tonight." The LLM could first search (via Perplexity's MCP integration) for the list of ingredients, then place the order and track it for you (via UberEats' MCP integration).
The incredible thing is that once these MCP integrations exist, you could realistically build the above example in a matter of hours from scratch.
To see the full LinkedIn post, click this link.
https://www.linkedin.com/posts/activity-7315709034366402560-2BmB
FAQs
1. What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) enables AI models, such as large language models (LLMs), to securely and efficiently connect with external data sources, tools, and services through a standardized interface.
2. Why was MCP created, and what problem does it solve?
MCP was created to address the complexity and fragmentation of integrating AI models with diverse external systems. Before MCP, each integration required custom code, leading to maintenance challenges and brittle systems. MCP standardizes these connections, making integrations more robust and scalable.
3. What are the core components of the MCP architecture?
MCP uses a client–host–server pattern:
Host: The application the user interacts with (e.g., an IDE or chatbot)
Client: Manages the connection to an MCP server
Server: Exposes tools, resources, and prompts via a standard API to the AI model.
4. What are ‘tools,’ ‘resources,’ and ‘prompts’ in MCP?
Tools: Executable functions (e.g., API calls, database queries) that the LLM can use.
Resources: Data sources or endpoints providing information (like files or logs).
Prompts: Predefined templates guiding how the model interacts with tools and resources.
5. How does MCP improve the reliability and maintainability of agentic AI systems?
By providing a universal protocol, MCP reduces the need for custom, one-off integrations and ensures that updates or changes in external tools can be managed at the server level, rather than requiring changes across every AI agent.
6. How does MCP handle security and privacy?
MCP is designed with a local-first security approach. It requires explicit user approval for access to tools or resources, and servers typically run locally unless remote access is permitted, ensuring sensitive data remains protected.
7. Who builds and maintains MCP integrations?
Service providers are responsible for building and maintaining their own MCP integrations. The open-source community also plays a key role, developing and maintaining open-source MCP implementations for broader adoption.
8. How is MCP being adopted in the AI ecosystem?
MCP has seen rapid adoption, with support from major companies like Anthropic, OpenAI, and AWS. Developer tools and platforms such as Cursor, Replit, and Sourcegraph have integrated MCP, and community-driven connectors are expanding its ecosystem.
9. What are some real-world examples of MCP in action?
With MCP, an LLM can interact with multiple services seamlessly. For example, it can search for recipe ingredients via one MCP integration, place an order through another, and track delivery—all from a single conversational interface.
Citations
Written by Chaz Englander
I’m not planning to become an educator, but people should really pay attention to this MCP (Model Context Protocol) chat—it's mind-bending. LLMs on their own aren't all that useful, which is why agents have exploded in popularity.
Agents are essentially LLMs with access to external tools.The problem with agentic systems that no one talks about is that as you layer on more capabilities, they become incredibly complex to maintain and often fail to work cohesively, leading to unreliable and brittle systems at scale. We should know; we've built arguably one of the most advanced agentic systems in the world.
Right now, each service provider speaks a different language as their APIs use different protocols, structures, request formats, etc. Plus, you have to constantly update each agent to keep up with new developments from each service provider, which is a huge hassle. This is where MCP comes in. MCP acts as a standardized layer between the LLM and external services, translating and simplifying communication.
So, how does the MCP ecosystem work, and who builds MCP? Anthropic (the inventors) set the standard, and it's up to service providers to build and maintain their own MCP integrations. If they don't, it makes their services difficult for LLMs to communicate with. However, some providers might argue they already have APIs and may hesitate to maintain their own MCP servers. This is where the open-source community comes in, developing and maintaining open-source MCP implementations, which is an exciting and powerful aspect of MCP.
Twelve months ago, it seemed almost inevitable that we needed a new protocol, but it was super unclear who would set the initial protocol and how it would become widely adopted, if at all. Two weeks ago, Sam Altman posted on X that "OpenAI people love MCP and we are excited to add support across our products" (which triggered this post). OpenAI enabling their Agents SDK to plug into MCP (alongside ChatGPT) makes it clear that MCP is poised to be the protocol of the future. Why is this so important?
Prior to MCP, if you wanted an LLM to work with an external tool (e.g. send an email, fetch a Slack message, perform a Google search, access a database), you had to build and maintain integrations for each one. Now, because MCP integrations standardize communication, adding new tools and maintaining them becomes infinitely simpler and far more robust. This means that soon you'll be able to interact with your entire digital ecosystem through a single application—retrieving information & performing actions.
For example: "Hey LLM, order the ingredients for a green Thai curry tonight." The LLM could first search (via Perplexity's MCP integration) for the list of ingredients, then place the order and track it for you (via UberEats' MCP integration).
The incredible thing is that once these MCP integrations exist, you could realistically build the above example in a matter of hours from scratch.
To see the full LinkedIn post, click this link.
https://www.linkedin.com/posts/activity-7315709034366402560-2BmB
FAQs
1. What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) enables AI models, such as large language models (LLMs), to securely and efficiently connect with external data sources, tools, and services through a standardized interface.
2. Why was MCP created, and what problem does it solve?
MCP was created to address the complexity and fragmentation of integrating AI models with diverse external systems. Before MCP, each integration required custom code, leading to maintenance challenges and brittle systems. MCP standardizes these connections, making integrations more robust and scalable.
3. What are the core components of the MCP architecture?
MCP uses a client–host–server pattern:
Host: The application the user interacts with (e.g., an IDE or chatbot)
Client: Manages the connection to an MCP server
Server: Exposes tools, resources, and prompts via a standard API to the AI model.
4. What are ‘tools,’ ‘resources,’ and ‘prompts’ in MCP?
Tools: Executable functions (e.g., API calls, database queries) that the LLM can use.
Resources: Data sources or endpoints providing information (like files or logs).
Prompts: Predefined templates guiding how the model interacts with tools and resources.
5. How does MCP improve the reliability and maintainability of agentic AI systems?
By providing a universal protocol, MCP reduces the need for custom, one-off integrations and ensures that updates or changes in external tools can be managed at the server level, rather than requiring changes across every AI agent.
6. How does MCP handle security and privacy?
MCP is designed with a local-first security approach. It requires explicit user approval for access to tools or resources, and servers typically run locally unless remote access is permitted, ensuring sensitive data remains protected.
7. Who builds and maintains MCP integrations?
Service providers are responsible for building and maintaining their own MCP integrations. The open-source community also plays a key role, developing and maintaining open-source MCP implementations for broader adoption.
8. How is MCP being adopted in the AI ecosystem?
MCP has seen rapid adoption, with support from major companies like Anthropic, OpenAI, and AWS. Developer tools and platforms such as Cursor, Replit, and Sourcegraph have integrated MCP, and community-driven connectors are expanding its ecosystem.
9. What are some real-world examples of MCP in action?
With MCP, an LLM can interact with multiple services seamlessly. For example, it can search for recipe ingredients via one MCP integration, place an order through another, and track delivery—all from a single conversational interface.
Citations
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