API vs MCP: What’s the difference and why it matters

AI assistants can draft a contract, summarise a negotiation or suggest the right clause in seconds but until recently, they couldn’t do much with that work. They couldn’t send the contract, check who had signed it or verify the identity of the person on the other side.

That is changing, and two acronyms sit at the centre of the shift: API and MCP.

They are often mentioned together and sometimes this can become confusing. But while they are both technically APIs, they are used to solve different problems. In this article, we explain what each one is, how they differ and we explain why this matters. This applies to any business using APIs and AI in real, legally binding agreement workflows.

API vs MCP: The key differences

The simplest way to put it: an API connects software to software, while MCP connects AI agents to software.

A few of these differences are worth expanding on.

Who is the user? An API expects a program on the other end, written by a developer who knows exactly what to call. MCP expects an AI model that is working on behalf of a person, interpreting their intent.

Fixed versus flexible. An API integration follows the path a developer built. If your onboarding process sends three documents in a set order, an API is ideal. MCP shines when the task varies from day to day, such as “find every NDA that hasn’t been signed this month and remind those people”.Build once, use widely. Traditionally, connecting a service to a new tool meant building a new integration. With MCP, a single server can serve several AI environments. The AI learns what the server can do by reading tool descriptions. So you don’t need a separate integration for each assistant.

MCP doesn’t replace APIs. It builds on them

While MCP is a new type of API, it is not the successor to existing APIs or a way to replace them. In most cases, an MCP server sits on top of existing APIs and translates AI requests into API calls.

Here is what typically happens:

• A user gives the AI assistant an instruction in plain language.
• The AI selects the relevant tool exposed by the MCP server.
• The MCP server turns that choice into a secure API request.
• The service performs the action and returns the result, which the AI presents to the user.

This means the reliability, security and compliance of the underlying API still matter just as much as before. MCP makes those capabilities accessible to AI; it doesn’t change what they are. For businesses, this is reassuring: the proven systems stay in place, and MCP adds a new, more natural way to use them.

Why this matters for agreements and identity

Most AI tools can draft text, but they can’t confirm who they are dealing with or guarantee that the outcome will hold up legally. A contract generated by AI is still only a draft until the right people have been identified and have signed it in a legally valid way.
This is the gap between generative AI and trusted business processes. Closing it requires two things that AI alone cannot provide: verified identity and legally binding signatures. That is why the service behind the MCP server matters as much as the protocol itself.

Introducing the Scrive MCP server

The Scrive MCP server brings Scrive’s trusted identity and eSigning into AI-driven workflows. It acts as a bridge between your AI tool and Scrive’s APIs, so AI agents can prepare documents, verify identities and collect legally binding signatures, all from a natural language request.

Because it builds on Scrive’s core products, agreements handled through the MCP server meet the same strict eIDAS requirements as the rest of the Scrive platform. Signers can be authenticated with trusted eIDs such as Swedish BankID, MitID and Finnish BankID, directly from an AI-driven process.

With the current version, AI agents can:

• search and filter documents by title, content, modification date or signing status
• create individual documents or bundles with multiple PDFs and signing parties
• add signing parties and start signing flows
• send reminders to people who haven’t signed yet
• retrieve usage statistics

In practice, that could look like this:

• Sales: “Prepare the Enterprise Agreement for TechCorp using our Q1 template, set authentication to Swedish BankID and send it to their CEO.”
• HR: “Show me which new hires haven’t signed their onboarding documents and remind those starting next Monday.”
• Legal: “Create a document bundle for our partner from these PDFs and start the signing process.”

The Scrive MCP server works with AI environments that support MCP, including Claude Desktop, ChatGPT and Copilot. Limits and parameters are determined by you and your setup. There are no additional AI-specific limits set by Scrive; the only limits are those in your existing Scrive subscription, such as document volume or SMS credits.

Should you use the API or the MCP server?

The answer depends on what you’re building, and many organisations will use both.

Choose the Scrive eSign API when you need a fixed, high-volume process embedded in your own product or system, such as automatically sending a signing request every time a customer completes an application.

Choose the Scrive MCP server when you want people to work with agreements through an AI assistant, handling varied, everyday tasks without writing code for each one.

Because the MCP server is built on Scrive’s APIs, both routes lead to the same trusted outcome: verified signers and legally binding agreements.