MCP EXPLAINED
MCP vs API: what’s the difference?
MCP servers and APIs are complementary, not competitors. An API is the contract software uses to reach a service. An MCP server sits in front of that capability so AI applications can discover its tools at runtime and let a model call them, and it usually calls the existing API to do the work.
MCP servers vs APIs side by side
Based on the MCP specification, version 2026-07-28.
| Feature | APIs (REST, GraphQL, gRPC) | MCP servers |
|---|---|---|
| Built for | Software that developers write against a documented contract. | AI applications (MCP clients) that let a language model use external tools and data. |
| How capabilities are found | Read the documentation or OpenAPI spec, then write client code ahead of time. | The client asks the server at runtime, for example with tools/list, and the model chooses what to use. |
| Interface | Endpoints and operations over HTTP, such as REST, GraphQL or gRPC. | Tools, resources and prompts exchanged as JSON-RPC messages over stdio or Streamable HTTP. |
| Who decides what to call | Application code, written in advance. | The model, within the tools the client exposes; the spec recommends a human who can deny tool calls. |
| Authentication | Whatever each provider defines, often API keys or OAuth 2.0. | HTTP servers that support authorization use OAuth 2.1, which the spec defines; how a local stdio server gets credentials is up to each implementation, often environment variables. |
| Cost drivers | The provider's own pricing for requests and usage. | The same underlying API usage, plus model tokens for tool definitions and results in context. |
How they work together
- A user asks an AI application for something, such as “summarize my open support tickets”.
- The MCP client lists the tools the connected MCP servers offer and passes them to the model.
- The model picks a tool and its arguments; the client can ask the user to approve the call.
- The MCP server runs the tool, usually by calling the provider’s existing API with the user’s credentials.
- The result returns to the model, which answers or chooses the next tool.
The API stays the source of truth for data, permissions and rate limits. MCP standardizes the layer between the model and those capabilities, so one server works with any compatible client.
MCP servers in API Tracker
Registry snapshot observed . “Verified” means API Tracker matched the entry to publisher evidence; most registry entries are not reviewed.
APIs with an official MCP server
Each of these providers publishes an MCP server for its own API. Compare the API profile with the MCP server to see what an agent can reach.
| Provider | MCP server | Transport |
|---|---|---|
 GitHub | Work with GitHub repositories, issues, and pull requests.MCP server → for GitHubAPI profile → for GitHub | Local (stdio), Remote (Streamable HTTP) |
 Notion | Connect AI assistants to Notion workspace content.MCP server → for NotionAPI profile → for Notion | Remote (SSE), Remote (Streamable HTTP) |
 Supabase | Work with Supabase projects through AI assistants.MCP server → for SupabaseAPI profile → for Supabase | Local (stdio), Remote (Streamable HTTP) |
 Atlassian | Connect AI assistants to Atlassian work and collaboration tools.MCP server → for AtlassianAPI profile → for Atlassian | Remote (Streamable HTTP) |
 Linear | Find and update Linear issues, projects, and comments.MCP server → for LinearAPI profile → for Linear | Remote (Streamable HTTP) |
 Figma | Bring design context from Figma into coding workflows.MCP server → for FigmaAPI profile → for Figma | Remote (Streamable HTTP) |
 PayPal | Interact with PayPal services through its API tools.MCP server → for PayPalAPI profile → for PayPal | Local (stdio) |
 Vercel | Bring Vercel projects and deployment context into coding workflows.MCP server → for VercelAPI profile → for Vercel | Remote (Streamable HTTP) |
 Zapier | Connect AI assistants to app workflows through Zapier MCP.MCP server → for ZapierAPI profile → for Zapier | Remote (Streamable HTTP) |
 Apideck | Connect agents to Apideck’s unified business APIs.MCP server → for ApideckAPI profile → for Apideck | Local (stdio), Remote (Streamable HTTP) |
 Exa | Search the web and retrieve content with Exa.MCP server → for ExaAPI profile → for Exa | Remote (Streamable HTTP) |
 Firecrawl | Search, crawl, and extract content from websites.MCP server → for FirecrawlAPI profile → for Firecrawl | Local (stdio), Remote (Streamable HTTP) |
Common questions
Will MCP replace APIs?
No. Most MCP servers call an existing API behind the scenes. MCP standardizes how AI applications discover and invoke those capabilities; the API still does the work and enforces its own permissions and limits.
How do I turn an API into an MCP server?
Expose a set of the API’s operations as MCP tools, each with a clear description and an input schema. If the API has an OpenAPI spec, tools can be generated from its operations, but a smaller set of task-level tools is usually easier for a model to use well. Run the server locally over stdio or host it over Streamable HTTP, adding OAuth 2.1 authorization if it needs user credentials.
Can an AI agent call an API without MCP?
Yes. Applications can define functions or tools directly for a model and call the API themselves. MCP makes those tool definitions portable, so one server works across compatible clients instead of being rebuilt for each one.
Does MCP cost more than calling the API directly?
The API usage is the same. Using MCP through a model adds tokens for the tool definitions and results the model reads, so large tool lists and verbose responses increase model cost.
What is the difference between an MCP server and an MCP client?
An MCP server offers tools, resources and prompts. An MCP client is the connector inside an AI application, such as a chat app or IDE, that talks to servers on the model’s behalf.