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:
- Discover what tools are available
- Execute tools with appropriate parameters
- 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..."- Claude recognizes the question requires legal research
- Claude checks what LawForge tools are available
- Claude calls the appropriate tool with parameters
- LawForge executes the query and returns results
- Claude synthesizes the results into a helpful response
MCP vs. Traditional APIs
| Aspect | Traditional API | MCP |
|---|---|---|
| Discovery | Read docs manually | Tools self-describe |
| Integration | Code each endpoint | Standardized protocol |
| AI Awareness | AI doesn't "know" the API | AI understands available tools |
| Updates | Requires code changes | Automatic 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.
Learn More
- MCP Specification - Official protocol documentation
- MCP on GitHub - Open source implementations