Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@TDZ C64 KnowledgeSearch for VIC-II sprite registers and their memory addresses"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
TDZ C64 Knowledge
MCP server for managing and searching Commodore 64 documentation. Ingest PDFs, text, Markdown, HTML, Excel, and web pages into a searchable knowledge base accessible via Claude Code or other MCP clients.
🚀 Quick Start
# 1. Install
python -m venv .venv
.venv\Scripts\activate
pip install -e .
# 2. Configure Claude Code
claude mcp add tdz-c64-knowledge -- .venv\Scripts\python.exe server.py
# 3. Add documents
.venv\Scripts\python.exe cli.py add-folder "C:\c64docs" --tags reference --recursive
# 4. Search via Claude Code
# Ask: "Search the C64 docs for VIC-II sprite registers"See QUICKSTART.md for detailed setup.
Features
Search & Retrieval
FTS5 full-text search - 480x faster queries (50ms vs 24s)
Semantic search - Find by meaning, not keywords (e.g., "movable objects" → "sprites")
RAG question answering - Answer questions by synthesizing docs with citations
Fuzzy search - Typo tolerance ("VIC2" → "VIC-II", "asembly" → "assembly")
Progressive refinement - Search within results to narrow down
Hybrid search - Combines keyword + semantic with configurable weighting
Similarity search - Discover related documentation automatically
Query preprocessing - NLTK stemming and stopword removal
Smart tagging - AI-powered tag suggestions by category
Table/code search - Search extracted tables and code blocks
Document Management
Multi-format - PDF, TXT, MD, HTML, Excel, web scraping
Duplicate detection - Content-based deduplication
Chunked retrieval - Get specific sections without loading entire docs
Metadata extraction - Author, subject, page numbers
Persistent index - Documents stay indexed between sessions
AI-Powered Features
Entity extraction - Extract hardware, memory addresses, instructions, concepts (5000x faster with C64 regex patterns)
Relationship mapping - Co-occurrence analysis with distance-based strength scoring
Document comparison - Side-by-side analysis with similarity scores
Natural language query translation - Parse queries into structured search parameters
Anomaly detection - ML-based baseline learning for URL-sourced content (3400+ docs/second)
Temporal analysis - Event detection, timeline construction, historical context (5 event types, 8 date formats)
Advanced visualizations - 3D knowledge graphs, hierarchical bundling, Sankey flow diagrams
Wiki Export (NEW in v2.23.15)
Static HTML wiki - Export entire knowledge base to browsable website
Document similarity map - 2D visualization using UMAP/t-SNE dimensionality reduction
Interactive timeline - Horizontal scrollable timeline with zoom levels and event filters
Knowledge graph - D3.js force-directed graph (178 entities, 20 relationships)
Enhanced UI - Explanation boxes, prominent ASK AI button, file type detection
Clickable clusters - Browse k-means clusters with linked documents
No server required - Pure client-side JavaScript, works offline
Full-text search - Fuse.js powered search across all content
See WIKI_EXPORT_GUIDE.md for usage
REST API (Optional)
27 endpoints - Full CRUD, search, analytics, export
OpenAPI/Swagger docs - Interactive API at
/api/docsAPI authentication - Secure via X-API-Key header
See docs/REST_API.md for details
Performance
Scalability - Tested to 5,000+ documents
Concurrent throughput - 5,712 queries/sec (10 workers)
Lazy loading - 100k+ document support
Search caching - 50-100x speedup for repeated queries
Installation (Windows)
Prerequisites
Python 3.10+ - https://python.org (check "Add Python to PATH")
uv (recommended) or pip:
pip install uv
Setup
cd C:\Users\YourName\mcp-servers\tdz-c64-knowledge
# Using uv (faster)
uv venv
.venv\Scripts\activate
uv pip install mcp pypdf rank-bm25 nltk
# Or using pip
python -m venv .venv
.venv\Scripts\activate
pip install mcp pypdf rank-bm25 nltk
# Test
python server.py # Press Ctrl+C to stopConfiguration
Claude Code
claude mcp add tdz-c64-knowledge -- C:\path\.venv\Scripts\python.exe C:\path\server.pyOr add to .claude/settings.json:
{
"mcpServers": {
"tdz-c64-knowledge": {
"command": "C:\\path\\.venv\\Scripts\\python.exe",
"args": ["C:\\path\\server.py"],
"env": {
"TDZ_DATA_DIR": "C:\\c64-knowledge-data"
}
}
}
}Claude Desktop
Add to %APPDATA%\Claude\claude_desktop_config.json:
{
"mcpServers": {
"tdz-c64-knowledge": {
"command": "C:\\path\\.venv\\Scripts\\python.exe",
"args": ["C:\\path\\server.py"],
"env": {
"TDZ_DATA_DIR": "C:\\c64-knowledge-data"
}
}
}
}Environment Variables
Variable | Description | Default |
| Database directory |
|
| Enable FTS5 search (recommended) |
|
| Enable semantic search |
|
| Sentence-transformers model |
|
| Enable BM25 fallback |
|
| Enable NLTK preprocessing |
|
| Enable fuzzy search |
|
| Fuzzy similarity (0-100) |
|
| Enable OCR for scanned PDFs |
|
| Max cached results |
|
| Cache TTL (seconds) |
|
| Document directory whitelist | None |
Search Features
FTS5 Full-Text Search (Recommended)
Enable with USE_FTS5=1 for maximum performance:
480x faster than BM25
Native SQLite BM25 ranking
Porter stemming tokenizer
Semantic Search
Enable with USE_SEMANTIC_SEARCH=1:
Meaning-based search (e.g., "movable objects" finds "sprites")
FAISS vector similarity with sentence-transformers
~7-16ms per query after embeddings built
Pre-build embeddings:
pip install sentence-transformers faiss-cpu
Phrase Search
Use double quotes for exact phrases:
search_docs(query='"VIC-II chip" registers')Fuzzy Search
Handles typos automatically with USE_FUZZY_SEARCH=1:
"VIC-I" → "VIC-II" (83% similarity)
"grafics" → "graphics" (88% similarity)
Configurable threshold (default: 80%)
OCR for Scanned PDFs
Automatic with USE_OCR=1:
Detects scanned PDFs (< 100 chars extracted)
Uses Tesseract OCR
Install:
pip install pytesseract pdf2image Pillow+ Tesseract binary~1-2 seconds per page
Temporal Analysis & Visualizations
Extract events, construct timelines, and visualize knowledge graphs.
Event Detection
Automatically detect significant events in documents:
5 Event Types - Product releases, company milestones, technical innovations, cultural events, version updates
8 Date Formats - Full dates, month-year, year ranges, decades, parenthetical dates
Confidence Scoring - Pattern matching with proximity-based confidence (0.0-1.0)
Entity Association - Automatically link entities to events
# Extract events from a document
result = kb.extract_document_events('doc_id', min_confidence=0.7)
# Returns: event_count, filtered_count, stored_count, events listTimeline Construction
Build chronological timelines with flexible querying:
Automatic Timeline Building - Chronologically sorted by date (YYYYMMDD integer sort)
Category Organization - Group by decade-type combinations (e.g., "1980s-release")
Importance Levels - 1-5 scale based on confidence
Date Range Filtering - Query events by year range, type, importance
# Build timeline from events
timeline_result = kb.build_timeline(min_confidence=0.5)
# Query timeline
timeline = kb.get_timeline(start_year=1980, end_year=1989, min_importance=3)
# Get historical context
context = kb.get_historical_context(year=1982, context_years=2)Interactive Visualizations
Generate interactive HTML visualizations with Plotly and NetworkX:
Timeline Visualizations:
Interactive Timeline - Horizontal timeline with zoom/pan, color-coded by event type
Event Network - Spring layout showing event relationships
Trend Charts - Multi-subplot dashboard (bar chart, stacked area, cumulative line)
Advanced Graph Visualizations:
3D Knowledge Graph - Interactive 3D entity-relationship graph with rotation controls
Hierarchical Bundling - Circular layout with curved edges bundled through center
Sankey Diagrams - Topic flow over time (decade or year grouping)
# Generate visualizations
kb.visualize_timeline(start_year=1980, end_year=1990, output_path="timeline.html")
kb.visualize_knowledge_graph_3d(max_entities=50, output_path="graph_3d.html")
kb.visualize_hierarchical_bundling(max_entities=30, output_path="bundling.html")
kb.visualize_topic_flow_sankey(time_period='decade', output_path="flow.html")MCP Tools for Timeline
4 timeline-specific MCP tools:
extract_document_events- Extract and store events from documentsget_timeline- Query chronological timeline with filterssearch_events_by_date- Search events by date range and typeget_historical_context- Get events around a specific year
See PHASE3_TEMPORAL_ANALYSIS.md for complete documentation.
Tools
62 MCP tools organized by category. Key tools listed below.
Search Tools
search_docs - Full-text search
search_docs(query="SID register", max_results=5, tags=["sid"])semantic_search - Meaning-based search
semantic_search(query="How do sprites work?", max_results=5)hybrid_search - Combined keyword + semantic
hybrid_search(query="SID chip", semantic_weight=0.7, max_results=10)answer_question - RAG-based Q&A with citations
answer_question(
question="How do I program sprites on the VIC-II?",
max_sources=5,
search_mode="auto"
)fuzzy_search - Typo-tolerant search
fuzzy_search(query="VIC2 asembly", similarity_threshold=80)search_within_results - Progressive refinement
# Broad search, then refine
results = search_docs(query="VIC-II", max_results=50)
refined = search_within_results(results, "sprite collision", max_results=5)find_similar - Find related documents
find_similar(doc_id="abc123", max_results=5)Document Management
add_document - Add a file
add_document(
filepath="C:/docs/c64_ref.pdf",
title="C64 Programmer's Reference",
tags=["reference", "memory-map"]
)add_documents_bulk - Bulk import
add_documents_bulk(
directory="C:/c64docs",
pattern="**/*.{pdf,txt}",
tags=["reference"],
recursive=true
)list_docs - List all documents
get_chunk - Get specific chunk
get_chunk(doc_id="abc123", chunk_id=5)remove_document - Remove a document
remove_documents_bulk - Bulk remove by IDs or tags
remove_documents_bulk(tags=["outdated"])check_updates - Check for file changes
check_updates(auto_update=false)URL Scraping
scrape_url - Scrape documentation website
scrape_url(
url="https://www.c64-wiki.com/wiki/VIC",
tags=["wiki"],
depth=2,
threads=5
)rescrape_document - Re-scrape for updates
rescrape_document(doc_id="abc123", force=false)check_url_updates - Check all scraped docs
check_url_updates(auto_rescrape=false, check_structure=true)AI & Analytics
extract_entities - Extract named entities
extract_entities(doc_id="abc123", confidence_threshold=0.6)search_entities - Search across entities
search_entities(query="VIC-II", entity_types=["hardware"])get_entity_analytics - Comprehensive entity statistics
extract_entity_relationships - Extract co-occurrences
extract_entity_relationships(doc_id="abc123", min_strength=0.3)search_entity_pair - Find docs with entity pair
search_entity_pair(entity1="VIC-II", entity2="sprite")compare_documents - Side-by-side comparison
compare_documents(doc_id_1="abc", doc_id_2="def", comparison_type="full")suggest_tags - AI-powered tag suggestions
suggest_tags(doc_id="abc123", confidence_threshold=0.6)get_tags_by_category - Browse tags by category
translate_query - Parse natural language queries
translate_query(query="find sprites on VIC-II chip")Export Tools
export_entities - Export to CSV/JSON
export_entities(format="csv", output_path="entities.csv", min_confidence=0.7)export_relationships - Export relationships
export_relationships(format="json", output_path="rels.json", min_strength=0.5)System
kb_stats - Knowledge base statistics
health_check - System diagnostics
Data Storage
SQLite database with 12+ tables:
documents - Document metadata
chunks - Chunked content (1500 words, 200 overlap)
document_tables - Extracted PDF tables
document_code_blocks - Detected code blocks
document_entities - Extracted entities
entity_relationships - Co-occurrence tracking
Plus: summaries, extraction_jobs, monitoring_history, etc.
Benefits:
Lazy loading (metadata at startup, chunks on-demand)
ACID transactions
Scalable to 100k+ documents
FTS5 full-text indexes
Default location: ~/.tdz-c64-knowledge or TDZ_DATA_DIR
Usage Examples
Ask Claude Code:
"Search the C64 docs for SID voice registers"
"What does the memory map say about $D400?"
"Find information about sprite multiplexing"
"Add C:/docs/mapping_the_c64.pdf with tags memory-map, reference"
"How do I program raster interrupts on the VIC-II?" (uses RAG)
Suggested Tags
Organize docs with consistent tags:
reference,memory-map,basic,assemblysid,vic-ii,cia,kernalhardware,disk,graphics,sound
Troubleshooting
"pypdf not installed" - Run: pip install pypdf rank-bm25
"mcp module not found" - Run: pip install mcp
Server not responding - Use Python from virtual environment, not system Python
PDF extraction issues - Use OCR or add plain text version
BM25 issues - Check logs in TDZ_DATA_DIR/server.log, try USE_BM25=0
Development
Testing
pip install -e ".[dev]"
# Run all tests
pytest test_server.py test_wiki_export.py -v
# With coverage
pytest test_server.py -v --cov=server --cov-report=term
# Wiki export tests only
pytest test_wiki_export.py -vTest Coverage:
test_server.py- Core server functionality (search, entities, RAG, etc.)test_wiki_export.py- Wiki generation features (16 tests):Document coordinate export (UMAP/t-SNE)
File type detection (HTML/MD)
Cluster document export
HTML generation with explanation boxes
JavaScript generation for interactive features
CI/CD
GitHub Actions workflow tests on Python 3.10/3.11/3.12 across Windows/Linux/macOS with Ruff code quality checks.
Documentation
Core Documentation
README.md (this file) - Installation, features, tools, usage
QUICKSTART.md - Fast setup guide (5 minutes)
ARCHITECTURE.md - Technical deep dive, database schema, algorithms
CONTEXT.md - Project status, quick stats, version history
CLAUDE.md - Quick reference for Claude Code integration
CHANGELOG.md - Complete version history
Feature Documentation
Browse docs/ for detailed guides on specific features:
API & Integration:
REST API - FastAPI REST server (27 endpoints)
AI-Powered Features:
Entity Extraction - Extract hardware, memory addresses, instructions
Anomaly Detection - ML-based URL content monitoring
Summarization - AI-powered document summarization
Data Sources:
Web Scraping - Scrape documentation websites
Web Monitoring - Track URL-sourced content changes
Setup & Deployment:
Deployment Guide - Production deployment
Docker Setup - Docker configuration
Environment Setup - Environment variables
Poppler Setup - Poppler installation for PDFs
User Interfaces:
GUI Guide - Streamlit web interface
Development:
Testing Guide - Test suite and CI/CD
Examples - Usage examples and performance analysis
Monitoring Setup - Scheduled monitoring configuration
Roadmap - Future improvements and features
Version History
v2.23.0 - RAG Question Answering & Advanced Search (Phase 2 Complete)
RAG-based answer_question with citations
Fuzzy search with rapidfuzz
Progressive search refinement
Smart tagging system
v2.22.0 - Search Improvements (Phase 1 Complete)
Enhanced entity analytics
C64-specific regex patterns (5000x faster)
Performance optimizations
v2.21.0 - Anomaly Detection
ML-based baseline learning
1500x performance improvement
v2.18.0 - REST API & Background Processing
FastAPI REST server (27 endpoints)
Background entity extraction
v2.15.0+ - Entity Intelligence
Entity extraction, relationships, analytics
See CONTEXT.md for complete version history.
License
MIT License - Use freely for your retro computing projects!
This server cannot be installed
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.