Why Model Context Protocol (MCP) Matters for Legal Tech: A Practical Overview

Large Language Models (LLMs) are impressive, but clever text alone doesn't transform legal work. The real value comes when models integrate smoothly with your existing tools. Anthropic’s Model Context Protocol (MCP) was recently launched to address exactly this challenge.

This article explains MCP clearly, how it fits with function calling, and why legal tech teams should pay attention.

What exactly is MCP?

MCP is an open integration standard released by Anthropic in late 2024. Think of MCP as a universal connector. Instead of building new integrations each time you update your model or tools, MCP gives a consistent way for your AI to communicate with external resources, like contract templates, clause libraries, or compliance databases.

MCP uses a simple client-server structure:

• Host: Your primary application (chatbot, document drafting tool, research app).
• Client: Manages interactions between the LLM and external resources.
• Server: Provides access to resources, like templates, clauses, or compliance data.

MCP vs. Function Calling

Function calling and MCP do different jobs:

• Function calling turns user requests ("Draft an NDA") into clear commands (e.g., generate_nda).
• MCP then executes those commands, retrieving the required information from your tools and systems.

Function calling is about interpreting what needs to happen, while MCP is about actually doing it.

Do you need function calling to use MCP?

You don't have to use function calling to make MCP work. MCP works independently, acting as a reliable integration layer that standardises communication between your LLM and external tools or databases.

Function calling helps because it structures natural language into clear instructions, making the workflow smoother, but it isn't necessary. You can trigger MCP directly through your applications without involving function calling.

Practical example: Automating contract creation

Here’s how MCP and function calling could work practically:

Step 1: User request

The lawyer types:

"Draft a confidentiality agreement between ABC Ltd. and Client Co."

The LLM uses function calling to turn this into a structured request:

{
  "function": "generate_confidentiality_agreement",
  "parameters": {
    "jurisdiction": "UK",
    "parties": ["ABC Ltd.", "Client Co."]
  }
}

Step 2: MCP execution

MCP receives the structured request and then:

• Fetches approved confidentiality clauses (fetch_clauses(type='confidentiality', jurisdiction='UK')).
• Validates these clauses against internal policies (validate_clauses()).
• Creates a formatted document and uploads it into your Contract Lifecycle Management (CLM) system.

Your lawyer instantly receives a compliant, ready-to-use draft.

Technical detail: How MCP structures requests

MCP uses clear JSON-RPC requests, simplifying integration:

{
  "jsonrpc": "2.0",
  "id": 1456,
  "method": "tools/call",
  "params": {
    "name": "generate_confidentiality_agreement",
    "arguments": {
      "jurisdiction": "UK",
      "parties": ["ABC Ltd.", "Client Co."]
    }
  }
}

This predictable format simplifies development and reduces long-term maintenance.

Less tool switching and more flexibility

Before MCP, teams managed multiple point solutions separately, like compliance checks, clause libraries, and document automation systems. MCP puts all these tools into one workspace, reducing context-switching and helping teams focus more on actual legal work.

MCP significantly improves flexibility, if you decide to replace your document automation provider, usually this kind of change would force rebuilding integrations. With MCP, your LLM communicates directly with the MCP server. That means the underlying provider can change, but for your lawyers or paralegals, nothing practically changes. As long as documents remain accurate, they won't even notice the change behind the scenes.

Better outcomes for clients with complaince built in

Clients value accuracy, speed, and clear communication. Using MCP helps lawyers deliver faster, higher quality services. With less manual work required for routine tasks, lawyers spend more time on strategic advice and client interactions. Clients directly benefit from quicker responses, consistent quality, and better-informed advice.

MCP also improves compliance by standardising consent checks or ethical standards. For example, consent verification before emailing clients can be automated reliably:

{
  "function": "verify_consent",
  "parameters": {
    "client_id": "5678",
    "action": "send_update_email"
  }
}

This automation ensures that ethical and compliance standards are consistently met.

Looking forward: Autonomous legal assistants

Combining MCP integration with structured function calling enables practical and genuinely useful autonomous legal tasks:

• Drafting and reviewing contracts automatically.
• Summarising extensive legal research swiftly.
• Real-time regulatory alerts directly to your team.

These practical uses aren't distant concepts, using MCP’s structured integration approach, you can start to work on them today.

Making AI practical for legal teams

MCP isn't flashy technology; it's the essential infrastructure for practical AI integration. It simplifies how in legal we can integrate and manage LLMs, reduceing disruption from changing providers, and directly improves legal service delivery.

Now is a great time to talk with your engineering teams about MCP and how it could help your firm connect previously siloed systems directly with your AI solutions. Ask them what integrations currently slow them down, identify areas where streamlined processes would make a real difference, and explore together how MCP can practically improve both the user experience and the way your lawyers deliver services to clients.