Build a Legal Document Analysis MCP Server for Contract Intelligence

Legal document analysis is one of the highest-value applications for AI agents. Law firms, corporate legal departments, and contract managers spend thousands of hours parsing documents that follow predictable structures — but those structures are buried in dense legal prose.

This MCP server gives AI agents the ability to analyze legal documents as easily as querying a database. Parse any contract, extract every clause, identify risk patterns, and compare terms across documents — all through standard MCP tool calls.

What This MCP Server Does

Five tools that cover the legal document analysis lifecycle:

1. parse_contract — Ingests PDFs, DOCX, or scanned documents. Uses OCR for scanned pages, then segments the document into legal sections (definitions, obligations, indemnification, termination, governing law).

2. extract_clauses — Pulls out every clause from a parsed contract with metadata: clause type, parties involved, obligations, deadlines, and conditions.

3. identify_risks — Scores each clause against a configurable risk library. Flags unlimited liability, broad indemnification, aggressive IP assignment, non-standard governing law, and other common risk patterns.

4. compare_contracts — Takes two or more contracts and produces a diff report showing which terms differ, which clauses are missing in one version, and which party each deviation favors.

5. summarize_obligations — Produces a timeline of all obligations extracted from a contract, with responsible parties, deadlines, and consequences for breach.

Implementation

Project Structure

legal-doc-mcp/
  server.py          # FastMCP server with 5 tools
  parsers/
    pdf_parser.py     # PDF extraction with OCR
    docx_parser.py    # DOCX extraction
  analyzers/
    clause_extractor.py
    risk_scorer.py
    contract_comparator.py
  config/
    risk_library.yaml # Configurable risk patterns

The MCP Server

# server.py
from fastmcp import FastMCP, Tool
from parsers.pdf_parser import PDFParser
from parsers.docx_parser import DOCXParser
from analyzers.clause_extractor import ClauseExtractor
from analyzers.risk_scorer import RiskScorer
from analyzers.contract_comparator import ContractComparator
import json

mcp = FastMCP(
    name="legal-doc-analyzer",
    version="1.0.0",
    tools=[
        Tool(
            name="parse_contract",
            description="Parse a legal document (PDF/DOCX) into structured sections",
            inputSchema={
                "type": "object",
                "properties": {
                    "file_path": {"type": "string", "description": "Path to the document"},
                    "file_type": {"type": "string", "enum": ["pdf", "docx"]},
                    "ocr_enabled": {"type": "boolean", "description": "Enable OCR for scanned documents", "default": True}
                },
                "required": ["file_path"]
            }
        ),
        Tool(
            name="extract_clauses",
            description="Extract all clauses from a parsed contract with metadata",
            inputSchema={
                "type": "object",
                "properties": {
                    "contract_id": {"type": "string", "description": "ID from parse_contract"},
                    "clause_types": {"type": "array", "description": "Filter to specific clause types", "items": {"type": "string"}}
                },
                "required": ["contract_id"]
            }
        ),
        Tool(
            name="identify_risks",
            description="Score contract clauses against risk patterns",
            inputSchema={
                "type": "object",
                "properties": {
                    "contract_id": {"type": "string", "description": "ID from parse_contract"},
                    "risk_profile": {"type": "string", "enum": ["aggressive", "balanced", "conservative"], "default": "balanced"}
                },
                "required": ["contract_id"]
            }
        ),
        Tool(
            name="compare_contracts",
            description="Compare two or more contracts and show differences",
            inputSchema={
                "type": "object",
                "properties": {
                    "contract_ids": {"type": "array", "items": {"type": "string"}, "description": "IDs from parse_contract"},
                    "focus_areas": {"type": "array", "items": {"type": "string"}, "description": "Specific areas to compare"}
                },
                "required": ["contract_ids"]
            }
        ),
        Tool(
            name="summarize_obligations",
            description="Extract and timeline all obligations from a contract",
            inputSchema={
                "type": "object",
                "properties": {
                    "contract_id": {"type": "string", "description": "ID from parse_contract"},
                    "party_filter": {"type": "string", "description": "Filter obligations by party"}
                },
                "required": ["contract_id"]
            }
        )
    ]
)

# Tool implementations
@app.tool()
async def parse_contract(file_path: str, file_type: str = "pdf", ocr_enabled: bool = True):
    """Parse a legal document into structured sections."""
    if file_type == "pdf":
        parser = PDFParser(ocr_enabled=ocr_enabled)
    else:
        parser = DOCXParser()

    result = await parser.parse(file_path)
    return {
        "contract_id": result.id,
        "sections": result.sections,
        "page_count": result.page_count,
        "section_count": len(result.sections)
    }


@app.tool()
async def identify_risks(contract_id: str, risk_profile: str = "balanced"):
    """Score contract clauses against risk patterns."""
    scorer = RiskScorer(profile=risk_profile)
    contract = await get_contract(contract_id)
    risks = await scorer.analyze(contract)
    return {
        "overall_risk_score": risks.score,
        "critical_risks": risks.critical,
        "high_risks": risks.high,
        "medium_risks": risks.medium,
        "recommendations": risks.recommendations
    }

Risk Library Configuration

# config/risk_library.yaml
risk_patterns:
  unlimited_liability:
    severity: critical
    patterns:
      - "no limitation on liability"
      - "liable for all damages"
      - "unlimited indemnification"
    alternative: "Cap liability at total contract value or 12 months of fees"

  broad_ip_assignment:
    severity: high
    patterns:
      - "all intellectual property.*work product.*assign"
      - "forever.*irrevocable.*all rights"
    alternative: "Limit IP assignment to specific deliverables"

  non_compete_overreach:
    severity: high
    patterns:
      - "non-compete.*\d+ years.*worldwide"
      - "shall not engage.*competing business"
    alternative: "Limit to 1 year, specific geography, and related services"

  governing_law_mismatch:
    severity: medium
    patterns:
      - "governing law.*foreign jurisdiction"
    alternative: "Negotiate governing law to home jurisdiction"

Connecting in Claude Code / Cursor

// .mcp.json
{
  "mcpServers": {
    "legal-doc-analyzer": {
      "command": "python",
      "args": ["server.py"],
      "env": {
        "RISK_LIBRARY_PATH": "config/risk_library.yaml",
        "OCR_ENGINE": "tesseract"
      }
    }
  }
}

Usage Examples

In Claude Code:

User: Analyze this vendor contract and flag any risky clauses
Claude: [calls parse_contract → extract_clauses → identify_risks]
       I found 3 critical risks:
       1. Unlimited liability clause (Section 8.2)
       2. Broad IP assignment (Section 12.1)
       3. Non-compete lasting 3 years worldwide (Section 14.3)

In Cursor:

User: Compare our standard MSA with the vendor's proposed version
Cursor: [calls compare_contracts]
        Differences found in 7 clauses:
        - Liability cap reduced from $2M to unlimited
        - IP clause expanded to include derivative works
        - Added 30-day termination for convenience

Key Metrics

Built by Deepak Bagada at DailyAIWorld.com. This MCP server is part of our MCP Directory — production-ready tools for AI agents.