Tree Analyzer MCP Server

Family tree analysis and error detection for Claude Code via MCP

Analyze genealogy trees for errors, duplicates, timeline issues, and missing sources through Claude's Model Context Protocol.

Features

• 🔍 Name Disambiguation - Detect duplicate persons using fuzzy matching and phonetic algorithms (Soundex, NYSIIS, Metaphone)

• ⏱️ Timeline Validation - Find impossible dates, age issues, and chronological errors

• 🔗 Relationship Checker - Detect circular ancestry and structural tree problems

• 📊 Source Coverage - Identify persons and events missing source citations

• 📝 Report Generation - Create detailed Markdown reports with FamilySearch links

• 🧬 Spanish Name Support - Optimized for Spanish/Latin American naming patterns

Related MCP server: VibeCheck MCP Server

Installation

Via Claude Desktop

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "tree-analyzer": {
      "command": "python",
      "args": ["-m", "tree_analyzer_mcp.server"],
      "cwd": "/path/to/tree-analyzer-mcp-standalone"
    }
  }
}

Via Docker

docker run -v /path/to/data:/app/data tree-analyzer-mcp

From Source

git clone https://github.com/ibarrajo/tree-analyzer-mcp
cd tree-analyzer-mcp
pip install -e .

Tools Available

Usage Examples

Detect Duplicate Names

Ask Claude Code:

Find potential duplicate persons in my family tree

Claude will use:

detect_name_duplicates(
    surname_filter="Smith",  # Optional: focus on specific surname
    similarity_threshold=0.8
)

Validate Timeline

Ask Claude Code:

Check my family tree for timeline errors and impossible dates

Claude will use:

validate_timeline(
    person_id=None,  # None = check entire tree
    max_parent_age=60,
    min_parent_age=14
)

Generate Audit Report

Ask Claude Code:

Create a comprehensive audit report for my 8-generation tree starting with person GK1Y-97Y

Claude will use:

generate_audit_report(
    root_person_id="GK1Y-97Y",
    generations=8,
    sections=["all"]
)

Find Missing Sources

Ask Claude Code:

Which ancestors are missing source citations?

Claude will use:

analyze_source_coverage(
    root_person_id="GK1Y-97Y",
    min_sources_per_person=1
)

Development

# Clone repository
git clone https://github.com/ibarrajo/tree-analyzer-mcp
cd tree-analyzer-mcp

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=src --cov-report=html

# Lint code
ruff check src
black --check src
mypy src

# Format code
black src
ruff check --fix src

Architecture

• Python 3.11+ - Modern Python with type hints

• FastMCP - Official Model Context Protocol framework for Python

• SQLite - Local database access (reads from familysearch-mcp cache)

• Fuzzy Matching - RapidFuzz for name similarity

• Phonetic Coding - Soundex, NYSIIS, Metaphone for name variants

• Jinja2 - Report templating

Data Flow

Claude Code
    ↓
MCP Protocol (stdio)
    ↓
tree-analyzer-mcp (Python)
    ↓
┌──────────────────┬──────────────────┐
│ familysearch-mcp │ research-sources │
│   cache.sqlite   │   -cache.sqlite  │
└──────────────────┴──────────────────┘
    ↓
Analysis & Reports

Name Disambiguation Algorithm

Optimized for Spanish/Latin American naming conventions:

1. Normalize: Remove accents, lowercase, strip particles (de, del, y)

2. Expand: Ma. → Maria, Fco. → Francisco, Jph → Joseph

3. Index: Compute Soundex, NYSIIS, Metaphone codes

4. Block: Match candidates via phonetic codes (avoids O(n²))

5. Score: Multi-factor similarity (0-1):

   • Surname exact: 0.25

   • Given name fuzzy: 0.20

   • Birth year proximity: 0.15

   • Birth place: 0.10

   • Death year proximity: 0.10

   • Parent names: 0.10

   • Spouse names: 0.05

   • Source overlap: 0.05

6. Cluster: Union-Find algorithm for grouping

7. Output: Disambiguation tables with timeline overlap

Tool Reference

detect_name_duplicates

Find potential duplicate persons using fuzzy matching.

Parameters:

• surname_filter (optional): Focus on specific surname

• similarity_threshold (optional): Minimum similarity score (0.0-1.0, default 0.75)

Returns: Markdown report with name clusters and similarity scores

validate_timeline

Check for impossible dates and age issues.

Parameters:

• person_id (optional): Check specific person (None = entire tree)

• max_parent_age (optional): Maximum age for having children (default 60)

• min_parent_age (optional): Minimum age for having children (default 14)

• max_lifespan (optional): Maximum human lifespan (default 120)

Returns: Markdown report with timeline errors and recommendations

check_relationships

Detect structural tree problems.

Parameters:

• person_id (optional): Starting person (None = check all)

• check_types (optional): Types of checks ["circular", "orphans", "marriages"]

Returns: Markdown report with relationship issues

analyze_source_coverage

Find persons and events missing sources.

Parameters:

• root_person_id (required): Starting person for analysis

• generations (optional): Number of generations to analyze (default 8)

• min_sources_per_person (optional): Minimum sources required (default 1)

Returns: Markdown report prioritized by generation proximity

generate_person_profile

Create detailed profile for one person.

Parameters:

• person_id (required): FamilySearch person ID

Returns: Markdown profile with all facts, relationships, sources

generate_audit_report

Comprehensive tree audit report.

Parameters:

• root_person_id (required): Starting person

• generations (optional): Depth to analyze (default 8)

• sections (optional): Sections to include (default ["all"])

Returns: Full Markdown audit with statistics, issues, recommendations

generate_research_leads

Prioritized research suggestions.

Parameters:

• root_person_id (required): Starting person

• focus_area (optional): "missing_sources", "timeline_errors", "duplicates"

Returns: Markdown report with actionable next steps

compare_persons

Deep comparison of two persons.

Parameters:

• person_id_a (required): First person ID

• person_id_b (required): Second person ID

Returns: Markdown comparison table with similarity score

Report Output

All reports are Markdown format with:

• Executive summary with issue counts by severity

• FamilySearch links for all persons (https://www.familysearch.org/tree/person/details/{PID})

• Actionable recommendations with specific next steps

• Statistics (person counts, source coverage, etc.)

• Tables for easy scanning of issues

Example report sections:

• Critical issues (circular ancestry, impossible dates)

• Name duplicate clusters with similarity scores

• Missing sources by generation

• Timeline validation results

• Research leads with collection suggestions

Contributing

See CONTRIBUTING.md for development guidelines.

License

MIT License - see LICENSE file for details.

Credits

Built as part of the FamilySearch genealogy research system. Part of a suite of MCP servers:

• familysearch-mcp - FamilySearch API integration

• tree-analyzer-mcp - Family tree analysis and error detection (this repository)

• research-sources-mcp - External source search

Links

• MCP Protocol Documentation

• FastMCP Framework

• FamilySearch Developer

Support

• Issues

• Discussions