MCP vs API – Why They’re Very Different and Why That Matters For You
MCP and API are both methods of integration that, on the surface, offer similar capabilities. They both provide a way to expose resources, including functionality and data from applications, to external clients, such as secondary applications and AI tools.
However, MCP and API are not the same. They diverge almost entirely in their approach to facilitating connectivity and in their primary applications.
Although at first sight, MCP might sound like a tweaked version of existing APIs, once you understand how it operates, you’ll see you are dealing with a very different animal, with its own advantages and risks for organizations that use it.
In the video below, MCP Manager CEO, Mike Yaroshefky, does a great job at discussing this confusion, pinpoints the significant differences between APIs and MCP servers, and explains why they matter for organizations that want to push forward and harness the power of AI:
My blog below provides all the information you need to understand the differences between API and MCP, why those differences matter, the scenarios in which APIs or MCP servers are most effective, and what your organization needs to do to adopt MCP servers successfully.
What is an API
An API (Application Programming Interface) is a combination of rules and protocols that allows different software applications to communicate, interact, and exchange information with each other.
Early examples of APIs date back to the 1940s, but the RESTful APIs used today began to proliferate in the early 2000s. REST is a set of architectural principles that standardize how APIs function.
APIs enable one software application to utilize the functions and/or data of another application, without requiring an understanding of its internal workings. You can think of an API as a bridge that gives different applications and other resources a meeting point, where they can pass data and instructions, without each resource needing to cross into the unfamiliar “territory” of the other.
This approach enables organizations to control, limit, and predetermine the manner of data transfer between applications and the automation of cross-application processes.
Organizations use APIs to automate processes, reconcile data across applications to maintain a single source of truth, and combine data from multiple applications and databases for comprehensive analytics.
Some key use cases of APIs include:
- Record Reconciliation: When a customer makes a payment in a system like Stripe, update their details in a CRM like Salesforce
- Cross-Application Notifications: Send Slack notifications when we get a new registration via an app like HubSpot
- Process (Webhook) Automation: For example, triggering email marketing campaigns in a platform like Mailchimp via API when a new contact is added to Salesforce or any other database.
- Authentication and Authorization: APIs enable single sign-on and third-party login functionality using services like OAuth, allowing apps to authenticate users securely via Facebook or Google accounts without handling passwords directly.
- Monitoring and Analytics: APIs collect telemetry and usage metrics from various applications and infrastructure, integrating with performance dashboards and alerting systems.
What is MCP
MCP or Model Context Protocol is an open-source technical standard that enables AI models to connect with apps, databases, internal systems, and other resources.
MCP enables AI agents to break free from the constraints of their typical chatbot-based setting and interact, communicate, and utilize services and datasets that are outside their training data, greatly enhancing the value they can deliver for enterprise users.
The MCP architecture consists of three key components: hosts, servers, and clients.
The host is the UI of the AI application that the human user interacts with, such as Claude Desktop or ChatGPT.
The client sits inside the host and maintains a 1:1 connection with the MCP server. It routes messages between the host and the server, acting as an intermediary and handling the communication protocol.
The server is external to the host and exposes specific tools, data, and prompts (tools are specific functions, for example, send_email, create_entry, search_dataset, etc).
Unlike REST APIs, the server does not use specific endpoints to expose tools; instead, it provides the client with JSON-based tool names and descriptions. It also provides context, hints, error messages, and other outputs to the client, all in an AI-friendly format.
When a MCP client and server are connected, and tools have been offered and selected, the client and server engage in a conversational, stateful dance of requests and responses, with context maintained by both parties through each subsequent request and response. This is a key differentiator from RESTful APIs.
Learn more in our article MCP servers explained.
Why Do People Confuse MCP and API
Technically, an MCP server is a specific form of Application Programming Interface (API), and MCP and API are both integration technologies that enable methods of connecting a second party to a resource, such as applications or databases.
However, when people talk about APIs, they’re really talking about a specific form of API – RESTful APIs – rather than the broad concept of API.
The RESTful APIs that we use every day to share data, workflows, and capabilities between different applications provide hard-set, static connections.
Conversely, MCP enables AI agents to connect to resources in dynamic and fluid ways, using runtime negotiation between the client and server to determine the right capabilities or data from each resource and how to best utilize them.
Therefore, it’s easy for people with very little knowledge of how MCP works to assume it is just a new iteration of the RESTful APIs that are already commonplace. However, this is a profound misunderstanding.
Within the realm of integration technologies, MCP takes a completely different approach to RESTful APIs, flipping the operating practices of staticity and rigidity around, in favour of fluidity, dynamism, and stateful, context-based interaction between client and server.
In summary, MCP and API are very different. Learn more about these differences below and the impact this has on end-users and organizations.
MCP vs API – What Are The Key Differences
| Dimension | APIs | MCP |
|---|---|---|
| Connection Methods | Hard-coded connections via documented endpoints; requires engineers to write code against API specifications; precise but brittle. | Real-time negotiation between AI and MCP server; server provides available tools and guidance dynamically, reducing brittleness. |
| Statefulness | Stateless; each request is independent and requires all context to be passed again, making multi-step workflows difficult and resource-heavy. | Stateful; maintains session-level context, enabling ongoing “conversations” and collaborative, multi-step workflows across requests. |
| Usage | Primarily connects applications for data exchange, automation, or simple integrations. Rigid, predefined, and not AI-friendly. | Designed for AI agents; supports dynamic tool discovery, context sharing, and agentic workflows to enhance AI capabilities. |
| Scalability | Highly scalable with proven techniques like load balancing, caching, and CDNs; decades of optimization. | Session-based design introduces scalability challenges; requires MCP gateways to filter tools, optimize sessions, and prevent AI overload at enterprise scale. |
| Security Challenges | Standard risks like token/key theft and endpoint interception; mitigated with OAuth, API keys, HTTPS, and token management. | New risks (tool poisoning, rug pulls, spoofing, session hijacking); requires runtime guardrails, metadata screening, and gateways for robust identity management. |
Rigid Connectors vs Fluid Choices
API connections require engineers to read product documentation and then write code that calls API endpoints as specified in that documentation. This makes calls precise, but also makes these connections difficult to configure, brittle, and prone to failure.
Changes to the client, resource, or API mean rewriting code and potentially going through the same cycle of trial and error again. Additionally, APIs do not adhere to a consistent protocol; for example, responses to calls may be returned in either JSON or XML. This inconsistency means each connection between a client and a resource is specific to that connection.
MCP combines documentation and invocation in a single package, which the AI accesses and utilizes in real-time. The MCP connection is negotiated and determined in real-time by the connecting AI and the MCP server. The MCP server acts like a concierge, or a helpful shop assistant, setting out the available tools and capabilities, and helping the AI to use them successfully.
Additionally, as the name suggests, the Model Context Protocol is a protocol. Consistency is essential to enabling the flexibility and dynamism it provides, and all MCP builders must ensure their servers adhere to the MCP specification.
Key Takeaway: API connections are static, predictable, and more labor-intensive to adjust. MCP connections are fluid and negotiated at runtime between the MCP client and server.
Stateful vs Stateless
APIs are typically stateless, meaning they don’t retain details of their last interaction for subsequent interactions.
Each request is independent and self-contained, requiring clients to manage all context and send all necessary context to the resource with each request. This makes complex, multi-step workflows difficult to achieve and resource-intensive.
MCP servers maintain session-level context; previous requests influence and shape how future requests are handled, and information can be combined across requests.
Session-level context allows MCP servers and clients to have an “ongoing conversation”, building a rich understanding of each task and working collaboratively. MCP enables complex and dynamic workflows, with rich, context-dependent decisions. However, this also creates challenges, particularly if the state/session-level context is confused or poisoned.
Key Takeaway: MCP facilitates an ongoing conversation between the client and server, enabling more complex and dynamic workflows that don’t require predefined configurations in advance.
Dynamic vs Defined
APIs expose fixed, predefined endpoints mapped to URLs. Using an API requires explicit, advanced knowledge of endpoints and their relationships. This offers consistency and stability, but also means the capabilities offered are limited and rigid when compared with MCP servers. Adding or modifying the functionality offered by the API requires updates and redeployments.
MCP enables clients to dynamically discover and select the capabilities of MCP servers at runtime, with the MCP server providing the client with its tool list, including descriptions, that the client can then select from. In MCP communication, clients can utilize tools in various ways, depending on the specific requirements of each task and its context.
Key Takeaway: MCPs enable AI agents to dynamically select the “tools” (capabilities) they need at runtime, as and when they are needed. This is a complete revolution of the static approach offered by APIs. Modifying capabilities is much less labor-intensive and disruptive in MCP servers compared to APIs.
Agents vs Applications
APIs primarily connect one application to the data and resources of another, enabling the execution of relatively simple automation, harmonizing data across applications, and combining data from different sources. People initially tried to connect AI agents to applications using custom, hardcoded APIs, but found the AI struggled with a contextless dancing partner and a rigid, non-conservational format.
MCP standardizes AI’s use of APIs and other integration technologies. MCP servers provide information in an AI-friendly format, enable dynamic tool discovery and use, and conduct an ongoing conversation with the client/AI agent (with shared session-level context), which enhances, rather than restricts or prohibits, the AI’s abilities.
Key Takeaway: Connecting AI agents to resources via APIs is possible, but not advisable; it’s error-prone and not scalable. MCP is a purpose-built protocol for agentic connectivity. MCP servers provide connections to resources in an AI-friendly manner, share the context management burden, and assist the AI in it work.
Scalability
APIs have demonstrated scalability over decades, with numerous techniques to enhance performance and prevent resource overloading, including load balancers, HTTP caching, and CDN (Content Delivery Networks), all of which help reduce latency and performance issues at scale.
MCP communication presents new scalability challenges for enterprises due to its session-based handling of communications (rather than handling each request separately). The dynamic methods used to present tools to clients also create challenges, as AIs can become overwhelmed and fail when given too many tools to choose from.
MCP gateways help organizations overcome these scalability challenges by optimizing MCP session-state management, filtering available tools to streamline tool selection, and imposing other restrictions to improve performance and reduce resource use.
Key Takeaway: API connections are simpler, more scalable by nature, and benefit from decades of development to optimize their performance. MCP communication creates new challenges, and middleware, such as MCP gateways, is a requirement for scalability.
Security Differences
API transactions, validate each request individually, using OAuth, API keys, and HTTPS. Most attacks on APIs attempt to intercept or otherwise obtain access tokens and API keys, which you can mitigate with strong endpoint authentication, robust token management, and additional authentication measures such as PKCE (Proof Key for Code Exchange).
MCP communications and agentic AI present new security risks and have spawned a range of new attack vectors, including tool poisoning, rug pull attacks, server spoofing, RADE, and cross-server shadowing, to name just a few.
Securing MCP server usage requires a mix of new approaches and longstanding methods. MCP security best practices include continuous screening of MCP tool metadata, prompt and output sanitization, runtime guardrails for user/AI agent behavior, permission-based controls (RBAC), and finely-scoped access tokens using OAuth.
The most common method for implementing all these security measures is an MCP gateway, such as MCP Manager.
Key Takeaway: The introduction of MCP and the AI agents that use it creates a range of new security threats and challenges around identity management and access controls. The stateful nature of MCP and the vulnerability of AI agents to manipulation necessitate a greater focus on runtime monitoring and moderation. You can achieve this by using an intermediary layer that sits between MCP clients and servers, typically an MCP gateway such as MCP Manager.
In Summary, MCP vs API Are Fundamentally Different Approaches
API and MCP are both integration technologies, but they are opposite ends of the spectrum in terms of approach, architecture, and capabilities.
API connections are simple, static, rigid exchanges of requests for and responses of information. They provide a stable and scalable method of communication and transfer between applications.
MCP connections are fluid and dynamic; they employ a conversational, stateful approach, which fits with the LLM-based agents that utilize them. MCP provides AI agents with the context and flexibility they need to utilize resources in increasingly complex and productive ways.
API connections are simpler, transactions are self-contained, and they benefit from decades of fine-tuning for performance, scalability, and security. MCPs are new, more complex and powerful, requiring new approaches to scalability and security, through MCP gateways/proxies like MCP Manager.
Where is it best to use MCP vs API?
Now that you’ve become acquainted with the main differences between MCP and API, you should start to see how each is better suited to different scenarios and types of connections. The sections below provide a summary of this for you.
What APIs Are Best For
APIs remain the best method for simpler application-to-application transactions, such as passing data between two apps, blending data from multiple apps, and automating basic, repeatable workflows.
Forward-thinking organizations are also examining how they can improve existing API-based workflows by using MCP-based approaches instead. However, these are in the exploratory phase, and most organizations will stick to APIs for application-to-application connections.
What MCP Servers Are Best For
MCP servers are purpose-built to enable AI agents, chatbots, and other AI-powered tools to use data, applications, internal systems, and other resources.
Connecting AI to resources via APIs has been a non-starter; it’s not scalable and won’t deliver value. If you are serious about harnessing the power of AI – especially agentic AI – at your organization, you need to use MCP servers to connect your AI agents to your resources.
Can I Secure MCP Servers Using API Security Tools?
No, the dynamic modes of interaction and communication that MCP facilitates between resources (via MCP servers) and MCP clients (or AI agents) create new security risks and attack vectors that you cannot satisfactorily mitigate with existing tools like API gateways.
How Can Enterprises Adopt and Use MCP Servers Securely
MCP gateways provide an intermediary layer that intercepts, moderates, logs, and optimizes all traffic between MCP clients and servers. An MCP gateway enables you to enforce security policies, scan and sanitize prompts and outputs, implement access controls, and more.
MCP gateways also provide a central point for creating, managing, and monitoring identities, authorization, and authentication flows for both users and AI agents in a consistent, standardized, and optimized manner.
Centralizing MCP traffic through a gateway is the only reliable way to protect your organization against MCP-based attacks and “irresponsible” AI agent behavior in runtime. As with any cybersecurity system, a gateway cannot provide “complete protection”, but it.
An MCP gateway has other important benefits for enterprises adopting MCP servers, such as:
- Enabling teams without engineering know-how to use MCP servers with their AI agents
- Controlling and pruning which MCP servers and tools are used (and by which users/agents)
- Providing end-to-end observability for security and performance
- Optimizing resource usage by MCP servers and AI agents.
- Enabling integration with existing security infrastructure and other business systems
In addition to MCP gateways, you should utilize existing network monitoring systems (such as Next-Generation Firewalls, Intrusion Prevention Systems, Intrusion Detection Systems or other platforms with deep packet inspection capabilities), to monitor network traffic for MCP use signatures and identity “shadow” MCP usage (use of MCP servers that have not been cleared and approved by your IT/information security team).
MCP and API – Very Different, But Good Partners?
To summarize, MCP and API are distinct technologies with different purposes. MCP should replace any existing attempts to connect LLM-based tools with resources, but is unlikely to replace simple app-app connectors, at least for the time being.
If your organization is seeking to harness the power of AI, you should first recognize and educate yourself on the differences between MCP and API. By reading this blog, you have already done that.
Next, you should determine what systems, processes, and training you need to adopt MCP servers in a manner that:
- Is secure
- Is scalable
- Is organized, controlled, and managed
- Enables use by non-technical users
A key component in each of these considerations is an MCP gateway, like MCP Manager. MCP gateways make your MCP server deployments:
- Secure
- Scalable
- Organized, controlled, and centrally managed
- Safely accessible (and less intimidating) for non-technical users
Schedule your 1-1 demonstration of MCP Manager now, or watch a short video overview first.
