Before MCP, connecting AI models to external tools was messy. Every integration was custom-built, which meant lots of duplicate work and inconsistent implementations.
This created what engineers call the "N×M problem" - if you have N different AI models and M different tools, you'd need N×M separate integrations. With 5 models and 10 tools, that's 50 different connections to build and maintain!
MCP-use solves this by creating one standard protocol that both models and tools can follow. Now, any model that speaks MCP can work with any tool that offers an MCP server.
What is MCP AI? It's this universal translator that lets AI models and external tools communicate using the same language. Instead of building custom connections for each combination, developers can focus on making their tools MCP-compatible once.
Large language models were previously isolated, limited to working with only the knowledge they were trained on. They couldn't access fresh data or perform actions in the real world. What's an MCP? It's the bridge that connects these isolated models to the outside world, letting them access current information and interact with other systems.
MCP Architecture Overview
MCP architecture has three main parts working together to connect AI models with external tools and data.
Key components:
Host: Where the AI model runs (like a chat interface or web app)
Client: The connector that handles communication with servers
Server: The tool or data source being accessed
Protocol: The communication rules (MCP itself)
This design separates responsibilities clearly. The AI model doesn't need to know how to talk to each tool directly - the client handles that translation work.
[Host/AI Model] ⇄ [Client] ⇄ [Server (Tool/Data Source)] ↕ [MCP Protocol]
MCP explained: Think of it like plugging different appliances into electrical outlets. The outlets (protocol) have a standard shape, so any appliance (tool) with the right plug can connect to the power grid (AI model).
Host And Client Roles
The host is where the AI model lives - this could be a website, desktop app, or development environment. The client sits between the host and external servers, handling all the communication details.
The client's job is to:
Format requests properly
Handle authentication
Manage connections to servers
Process responses
What is an MCP client? It's like a universal remote control for the AI. The model just needs to say what it wants to do, and the client figures out how to communicate that to the right server.
M.C.P. clients abstract away the technical details, so the AI can focus on understanding the user's needs rather than worrying about protocol specifics.
The Server Layer
MCP servers connect to external tools and data sources. Each server focuses on specific capabilities - one might connect to a database, another to a weather service, and another to a file system.
What is MCP server? It's the component that receives requests from clients, performs the requested actions, and sends back results. Servers are responsible for:
Executing tool functions
Retrieving and formatting data
Enforcing security rules
Handling errors gracefully
M C P servers can be combined to give AI models access to many different capabilities. For example, Claude might connect to a GitHub server to check code, a database server to run queries, and a browser server to look up information online.
Protocol Layer Basics
The protocol layer is the common language that clients and servers use to communicate. MCP uses JSON-RPC 2.0, which provides a structured format for requests and responses.
Model context protocol explained: It's a set of rules for how messages should be formatted, sent, and received. These rules ensure that any client can talk to any server as long as they both follow the protocol.
The protocol supports different transport methods:
HTTP and Server-Sent Events (SSE) for remote connections
Standard input/output (stdio) for local connections
This flexibility lets MCP work in various environments, from local desktop apps to cloud services.
How Does MCP Work Step By Step
When you ask an AI to do something that requires external tools, MCP follows a clear sequence of steps to make it happen.
Handshake And Capability Negotiation
The process starts with a handshake between the client and server. This is where they establish a connection and share information about what they can do.
During this phase:
The client connects to the server
They check that they're using compatible protocol versions
The server tells the client what tools it offers
The client registers these tools so the AI can use them
MCP means Model Context Protocol - it's the framework that makes this negotiation possible. MCP AI abbreviation refers to this same protocol that lets AI models discover and use external tools.
The handshake is important because it lets the AI know what capabilities are available before it tries to use them. This prevents errors from trying to use tools that don't exist.
Request And Response Flow
Once the handshake is complete, the AI can start using tools. Here's what happens when you ask the AI to check the weather:
You ask: "What's the weather in Chicago?"
The AI recognizes it needs external data
The client formats a request to the weather tool
The server receives the request and fetches weather data
The server sends the data back to the client
The client delivers the information to the AI
The AI incorporates this data into its response to you
What's MCP doing here? It's providing the structured communication channel that makes this whole process reliable. M C P meaning in this context is about creating a consistent way for the AI to access external information.
Security Tokens And Authentication
MCP includes security features to protect data and control access. When a client connects to a server, it typically needs to authenticate - proving it has permission to use the tools.
This usually involves:
Authentication tokens or API keys
Permission checks for specific actions
Data isolation between users
Privacy controls to limit data exposure
The security model ensures that tools can be used safely, with appropriate restrictions on what actions are allowed and what data can be accessed.
Large-Scale MCP-Use And Dynamic Loading
Many APIs have hundreds of endpoints - too many to load all at once. MCP provides ways to handle these large-scale integrations efficiently.
Dynamic Tool Registration
Instead of loading all tools at startup, MCP supports registering tools dynamically as needed. This means:
Tools can be added or removed during a session
Only relevant tools are loaded
Memory usage stays efficient
Startup time remains fast
MCP-use in this context refers to the practical application of the protocol in real-world scenarios with many tools.
Handling Many Endpoints
When working with large APIs, organization becomes crucial. Here are some strategies for managing many endpoints:
Group related tools: Organize tools by function or domain
Use hierarchical naming: Create logical categories for tools
Implement selective loading: Only load tools relevant to the current task
Cache frequently used tools: Improve performance for common operations
These approaches help maintain performance even when dealing with complex APIs that have hundreds of possible actions.
Why MCP Matters For AI Integrations
MCP creates a standard way for AI models to interact with external tools and data. This standardization has several important benefits.
First, it reduces development time. Instead of building custom integrations for every combination of AI model and tool, developers can implement the MCP standard once and gain compatibility with the entire ecosystem.
Second, it improves reliability. With a consistent protocol, there are fewer special cases and edge conditions to handle. This means fewer bugs and more predictable behavior.
Third, it enables composition. Different tools can be combined easily because they all speak the same language. An AI can use multiple tools in sequence to solve complex problems.
What is the MCP? It's the foundation that makes all of this possible - a shared language for AI systems and external tools to communicate effectively.
MCP explained: It transforms AI models from isolated text generators into capable assistants that can interact with the digital world around them.
Building MCP Servers with Stainless
At Stainless, we help organizations deliver high-quality MCP server experiences by transforming OpenAPI specifications into robust, production-ready implementations. Our approach automates the process while giving you full control over the generated code.
.mcp has become an important standard in the AI ecosystem, enabling more powerful and flexible applications. MCP means that AI models can now access the tools and data they need to be truly useful in real-world scenarios.
Get started for free at https://app.stainless.com/signup
Frequently Asked Questions About MCP
How do I handle versioning with MCP?
MCP includes version negotiation during the initial handshake between client and server. Both sides indicate which protocol versions they support, and they agree on a compatible version to use for communication.
Can I use MCP with existing OpenAPI specs?
Yes, existing OpenAPI specifications can be converted to MCP servers. This process involves transforming API endpoints into MCP tools and resolving any references in the schema definitions.
How does MCP differ from function calling APIs?
Unlike proprietary function calling APIs tied to specific providers, MCP is an open standard that works across different AI models and platforms, offering greater interoperability and flexibility.
Is MCP suitable for enterprise applications?
MCP's security model, scalability features, and standardization make it appropriate for enterprise use, especially when implemented with proper authentication and authorization systems.
Before MCP, connecting AI models to external tools was messy. Every integration was custom-built, which meant lots of duplicate work and inconsistent implementations.
This created what engineers call the "N×M problem" - if you have N different AI models and M different tools, you'd need N×M separate integrations. With 5 models and 10 tools, that's 50 different connections to build and maintain!
MCP-use solves this by creating one standard protocol that both models and tools can follow. Now, any model that speaks MCP can work with any tool that offers an MCP server.
What is MCP AI? It's this universal translator that lets AI models and external tools communicate using the same language. Instead of building custom connections for each combination, developers can focus on making their tools MCP-compatible once.
Large language models were previously isolated, limited to working with only the knowledge they were trained on. They couldn't access fresh data or perform actions in the real world. What's an MCP? It's the bridge that connects these isolated models to the outside world, letting them access current information and interact with other systems.
MCP Architecture Overview
MCP architecture has three main parts working together to connect AI models with external tools and data.
Key components:
Host: Where the AI model runs (like a chat interface or web app)
Client: The connector that handles communication with servers
Server: The tool or data source being accessed
Protocol: The communication rules (MCP itself)
This design separates responsibilities clearly. The AI model doesn't need to know how to talk to each tool directly - the client handles that translation work.
[Host/AI Model] ⇄ [Client] ⇄ [Server (Tool/Data Source)] ↕ [MCP Protocol]
MCP explained: Think of it like plugging different appliances into electrical outlets. The outlets (protocol) have a standard shape, so any appliance (tool) with the right plug can connect to the power grid (AI model).
Host And Client Roles
The host is where the AI model lives - this could be a website, desktop app, or development environment. The client sits between the host and external servers, handling all the communication details.
The client's job is to:
Format requests properly
Handle authentication
Manage connections to servers
Process responses
What is an MCP client? It's like a universal remote control for the AI. The model just needs to say what it wants to do, and the client figures out how to communicate that to the right server.
M.C.P. clients abstract away the technical details, so the AI can focus on understanding the user's needs rather than worrying about protocol specifics.
The Server Layer
MCP servers connect to external tools and data sources. Each server focuses on specific capabilities - one might connect to a database, another to a weather service, and another to a file system.
What is MCP server? It's the component that receives requests from clients, performs the requested actions, and sends back results. Servers are responsible for:
Executing tool functions
Retrieving and formatting data
Enforcing security rules
Handling errors gracefully
M C P servers can be combined to give AI models access to many different capabilities. For example, Claude might connect to a GitHub server to check code, a database server to run queries, and a browser server to look up information online.
Protocol Layer Basics
The protocol layer is the common language that clients and servers use to communicate. MCP uses JSON-RPC 2.0, which provides a structured format for requests and responses.
Model context protocol explained: It's a set of rules for how messages should be formatted, sent, and received. These rules ensure that any client can talk to any server as long as they both follow the protocol.
The protocol supports different transport methods:
HTTP and Server-Sent Events (SSE) for remote connections
Standard input/output (stdio) for local connections
This flexibility lets MCP work in various environments, from local desktop apps to cloud services.
How Does MCP Work Step By Step
When you ask an AI to do something that requires external tools, MCP follows a clear sequence of steps to make it happen.
Handshake And Capability Negotiation
The process starts with a handshake between the client and server. This is where they establish a connection and share information about what they can do.
During this phase:
The client connects to the server
They check that they're using compatible protocol versions
The server tells the client what tools it offers
The client registers these tools so the AI can use them
MCP means Model Context Protocol - it's the framework that makes this negotiation possible. MCP AI abbreviation refers to this same protocol that lets AI models discover and use external tools.
The handshake is important because it lets the AI know what capabilities are available before it tries to use them. This prevents errors from trying to use tools that don't exist.
Request And Response Flow
Once the handshake is complete, the AI can start using tools. Here's what happens when you ask the AI to check the weather:
You ask: "What's the weather in Chicago?"
The AI recognizes it needs external data
The client formats a request to the weather tool
The server receives the request and fetches weather data
The server sends the data back to the client
The client delivers the information to the AI
The AI incorporates this data into its response to you
What's MCP doing here? It's providing the structured communication channel that makes this whole process reliable. M C P meaning in this context is about creating a consistent way for the AI to access external information.
Security Tokens And Authentication
MCP includes security features to protect data and control access. When a client connects to a server, it typically needs to authenticate - proving it has permission to use the tools.
This usually involves:
Authentication tokens or API keys
Permission checks for specific actions
Data isolation between users
Privacy controls to limit data exposure
The security model ensures that tools can be used safely, with appropriate restrictions on what actions are allowed and what data can be accessed.
Large-Scale MCP-Use And Dynamic Loading
Many APIs have hundreds of endpoints - too many to load all at once. MCP provides ways to handle these large-scale integrations efficiently.
Dynamic Tool Registration
Instead of loading all tools at startup, MCP supports registering tools dynamically as needed. This means:
Tools can be added or removed during a session
Only relevant tools are loaded
Memory usage stays efficient
Startup time remains fast
MCP-use in this context refers to the practical application of the protocol in real-world scenarios with many tools.
Handling Many Endpoints
When working with large APIs, organization becomes crucial. Here are some strategies for managing many endpoints:
Group related tools: Organize tools by function or domain
Use hierarchical naming: Create logical categories for tools
Implement selective loading: Only load tools relevant to the current task
Cache frequently used tools: Improve performance for common operations
These approaches help maintain performance even when dealing with complex APIs that have hundreds of possible actions.
Why MCP Matters For AI Integrations
MCP creates a standard way for AI models to interact with external tools and data. This standardization has several important benefits.
First, it reduces development time. Instead of building custom integrations for every combination of AI model and tool, developers can implement the MCP standard once and gain compatibility with the entire ecosystem.
Second, it improves reliability. With a consistent protocol, there are fewer special cases and edge conditions to handle. This means fewer bugs and more predictable behavior.
Third, it enables composition. Different tools can be combined easily because they all speak the same language. An AI can use multiple tools in sequence to solve complex problems.
What is the MCP? It's the foundation that makes all of this possible - a shared language for AI systems and external tools to communicate effectively.
MCP explained: It transforms AI models from isolated text generators into capable assistants that can interact with the digital world around them.
Building MCP Servers with Stainless
At Stainless, we help organizations deliver high-quality MCP server experiences by transforming OpenAPI specifications into robust, production-ready implementations. Our approach automates the process while giving you full control over the generated code.
.mcp has become an important standard in the AI ecosystem, enabling more powerful and flexible applications. MCP means that AI models can now access the tools and data they need to be truly useful in real-world scenarios.
Get started for free at https://app.stainless.com/signup
Frequently Asked Questions About MCP
How do I handle versioning with MCP?
MCP includes version negotiation during the initial handshake between client and server. Both sides indicate which protocol versions they support, and they agree on a compatible version to use for communication.
Can I use MCP with existing OpenAPI specs?
Yes, existing OpenAPI specifications can be converted to MCP servers. This process involves transforming API endpoints into MCP tools and resolving any references in the schema definitions.
How does MCP differ from function calling APIs?
Unlike proprietary function calling APIs tied to specific providers, MCP is an open standard that works across different AI models and platforms, offering greater interoperability and flexibility.
Is MCP suitable for enterprise applications?
MCP's security model, scalability features, and standardization make it appropriate for enterprise use, especially when implemented with proper authentication and authorization systems.
Before MCP, connecting AI models to external tools was messy. Every integration was custom-built, which meant lots of duplicate work and inconsistent implementations.
This created what engineers call the "N×M problem" - if you have N different AI models and M different tools, you'd need N×M separate integrations. With 5 models and 10 tools, that's 50 different connections to build and maintain!
MCP-use solves this by creating one standard protocol that both models and tools can follow. Now, any model that speaks MCP can work with any tool that offers an MCP server.
What is MCP AI? It's this universal translator that lets AI models and external tools communicate using the same language. Instead of building custom connections for each combination, developers can focus on making their tools MCP-compatible once.
Large language models were previously isolated, limited to working with only the knowledge they were trained on. They couldn't access fresh data or perform actions in the real world. What's an MCP? It's the bridge that connects these isolated models to the outside world, letting them access current information and interact with other systems.
MCP Architecture Overview
MCP architecture has three main parts working together to connect AI models with external tools and data.
Key components:
Host: Where the AI model runs (like a chat interface or web app)
Client: The connector that handles communication with servers
Server: The tool or data source being accessed
Protocol: The communication rules (MCP itself)
This design separates responsibilities clearly. The AI model doesn't need to know how to talk to each tool directly - the client handles that translation work.
[Host/AI Model] ⇄ [Client] ⇄ [Server (Tool/Data Source)] ↕ [MCP Protocol]
MCP explained: Think of it like plugging different appliances into electrical outlets. The outlets (protocol) have a standard shape, so any appliance (tool) with the right plug can connect to the power grid (AI model).
Host And Client Roles
The host is where the AI model lives - this could be a website, desktop app, or development environment. The client sits between the host and external servers, handling all the communication details.
The client's job is to:
Format requests properly
Handle authentication
Manage connections to servers
Process responses
What is an MCP client? It's like a universal remote control for the AI. The model just needs to say what it wants to do, and the client figures out how to communicate that to the right server.
M.C.P. clients abstract away the technical details, so the AI can focus on understanding the user's needs rather than worrying about protocol specifics.
The Server Layer
MCP servers connect to external tools and data sources. Each server focuses on specific capabilities - one might connect to a database, another to a weather service, and another to a file system.
What is MCP server? It's the component that receives requests from clients, performs the requested actions, and sends back results. Servers are responsible for:
Executing tool functions
Retrieving and formatting data
Enforcing security rules
Handling errors gracefully
M C P servers can be combined to give AI models access to many different capabilities. For example, Claude might connect to a GitHub server to check code, a database server to run queries, and a browser server to look up information online.
Protocol Layer Basics
The protocol layer is the common language that clients and servers use to communicate. MCP uses JSON-RPC 2.0, which provides a structured format for requests and responses.
Model context protocol explained: It's a set of rules for how messages should be formatted, sent, and received. These rules ensure that any client can talk to any server as long as they both follow the protocol.
The protocol supports different transport methods:
HTTP and Server-Sent Events (SSE) for remote connections
Standard input/output (stdio) for local connections
This flexibility lets MCP work in various environments, from local desktop apps to cloud services.
How Does MCP Work Step By Step
When you ask an AI to do something that requires external tools, MCP follows a clear sequence of steps to make it happen.
Handshake And Capability Negotiation
The process starts with a handshake between the client and server. This is where they establish a connection and share information about what they can do.
During this phase:
The client connects to the server
They check that they're using compatible protocol versions
The server tells the client what tools it offers
The client registers these tools so the AI can use them
MCP means Model Context Protocol - it's the framework that makes this negotiation possible. MCP AI abbreviation refers to this same protocol that lets AI models discover and use external tools.
The handshake is important because it lets the AI know what capabilities are available before it tries to use them. This prevents errors from trying to use tools that don't exist.
Request And Response Flow
Once the handshake is complete, the AI can start using tools. Here's what happens when you ask the AI to check the weather:
You ask: "What's the weather in Chicago?"
The AI recognizes it needs external data
The client formats a request to the weather tool
The server receives the request and fetches weather data
The server sends the data back to the client
The client delivers the information to the AI
The AI incorporates this data into its response to you
What's MCP doing here? It's providing the structured communication channel that makes this whole process reliable. M C P meaning in this context is about creating a consistent way for the AI to access external information.
Security Tokens And Authentication
MCP includes security features to protect data and control access. When a client connects to a server, it typically needs to authenticate - proving it has permission to use the tools.
This usually involves:
Authentication tokens or API keys
Permission checks for specific actions
Data isolation between users
Privacy controls to limit data exposure
The security model ensures that tools can be used safely, with appropriate restrictions on what actions are allowed and what data can be accessed.
Large-Scale MCP-Use And Dynamic Loading
Many APIs have hundreds of endpoints - too many to load all at once. MCP provides ways to handle these large-scale integrations efficiently.
Dynamic Tool Registration
Instead of loading all tools at startup, MCP supports registering tools dynamically as needed. This means:
Tools can be added or removed during a session
Only relevant tools are loaded
Memory usage stays efficient
Startup time remains fast
MCP-use in this context refers to the practical application of the protocol in real-world scenarios with many tools.
Handling Many Endpoints
When working with large APIs, organization becomes crucial. Here are some strategies for managing many endpoints:
Group related tools: Organize tools by function or domain
Use hierarchical naming: Create logical categories for tools
Implement selective loading: Only load tools relevant to the current task
Cache frequently used tools: Improve performance for common operations
These approaches help maintain performance even when dealing with complex APIs that have hundreds of possible actions.
Why MCP Matters For AI Integrations
MCP creates a standard way for AI models to interact with external tools and data. This standardization has several important benefits.
First, it reduces development time. Instead of building custom integrations for every combination of AI model and tool, developers can implement the MCP standard once and gain compatibility with the entire ecosystem.
Second, it improves reliability. With a consistent protocol, there are fewer special cases and edge conditions to handle. This means fewer bugs and more predictable behavior.
Third, it enables composition. Different tools can be combined easily because they all speak the same language. An AI can use multiple tools in sequence to solve complex problems.
What is the MCP? It's the foundation that makes all of this possible - a shared language for AI systems and external tools to communicate effectively.
MCP explained: It transforms AI models from isolated text generators into capable assistants that can interact with the digital world around them.
Building MCP Servers with Stainless
At Stainless, we help organizations deliver high-quality MCP server experiences by transforming OpenAPI specifications into robust, production-ready implementations. Our approach automates the process while giving you full control over the generated code.
.mcp has become an important standard in the AI ecosystem, enabling more powerful and flexible applications. MCP means that AI models can now access the tools and data they need to be truly useful in real-world scenarios.
Get started for free at https://app.stainless.com/signup
Frequently Asked Questions About MCP
How do I handle versioning with MCP?
MCP includes version negotiation during the initial handshake between client and server. Both sides indicate which protocol versions they support, and they agree on a compatible version to use for communication.
Can I use MCP with existing OpenAPI specs?
Yes, existing OpenAPI specifications can be converted to MCP servers. This process involves transforming API endpoints into MCP tools and resolving any references in the schema definitions.
How does MCP differ from function calling APIs?
Unlike proprietary function calling APIs tied to specific providers, MCP is an open standard that works across different AI models and platforms, offering greater interoperability and flexibility.
Is MCP suitable for enterprise applications?
MCP's security model, scalability features, and standardization make it appropriate for enterprise use, especially when implemented with proper authentication and authorization systems.