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Model Context Protocol (MCP)

How MCP enables AI assistants to access external tools and data

Model Context Protocol (MCP)

The Model Context Protocol is an open standard that allows AI assistants like Claude to connect to external tools and data sources securely. LawForge uses MCP to provide legal research capabilities.

Why MCP Exists

Large language models like Claude have vast knowledge, but they face limitations:

  • Knowledge cutoff - Training data has a fixed date
  • No real-time data - Can't access current information
  • No specialized tools - Can't perform domain-specific operations

MCP solves these problems by creating a standard way for AI assistants to:

  1. Discover what tools are available
  2. Execute tools with appropriate parameters
  3. Receive structured results

How MCP Works

When you ask Claude a legal research question with LawForge connected:

sequenceDiagram
    participant User
    participant Claude
    participant LawForge

    User->>Claude: "Find Supreme Court cases about free speech"
    Claude->>LawForge: tools/list (discover available tools)
    LawForge-->>Claude: [legal_research, get_document, ...]
    Claude->>LawForge: tools/call legal_research(query="free speech", court="scotus")
    LawForge-->>Claude: {results: [...]}
    Claude->>User: "I found 47 relevant cases..."
  1. Claude recognizes the question requires legal research
  2. Claude checks what LawForge tools are available
  3. Claude calls the appropriate tool with parameters
  4. LawForge executes the query and returns results
  5. Claude synthesizes the results into a helpful response

MCP vs. Traditional APIs

AspectTraditional APIMCP
DiscoveryRead docs manuallyTools self-describe
IntegrationCode each endpointStandardized protocol
AI AwarenessAI doesn't "know" the APIAI understands available tools
UpdatesRequires code changesAutomatic via protocol

Security Model

MCP includes security by design:

  • User consent - Tools are only available when users explicitly connect them
  • Scoped access - Tools only have access to what they're designed for
  • Auditability - All tool calls are logged and visible
  • Sandboxing - Tools can't access arbitrary system resources

The Future of AI + Tools

MCP represents a shift in how AI assistants work:

Before MCP: AI assistants are closed systems with static knowledge.

With MCP: AI assistants become orchestrators that coordinate specialized tools to solve complex problems.

For legal research, this means Claude can combine:

  • Case search
  • Document retrieval
  • Citation analysis
  • Regulatory search

...into a seamless research workflow, rather than requiring users to manually coordinate separate tools.

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