Tibia Server

A tool that downloads, parses, and indexes all content from TibiaWiki — the wiki for the MMORPG Tibia — and serves it via an MCP (Model Context Protocol) server for AI agents.

Quick Start

The fastest way to get everything running is with Claude Code (Anthropic's official CLI). This project includes a custom skill /start that automates the entire setup:

1. Open a terminal at the project root
2. Start Claude Code: claude
3. Run the skill:

/start

The skill will automatically:

1. Check all prerequisites (Docker, Docker Compose, free ports, etc.)
2. Create the persistent data directory for PostgreSQL
3. Start the database, run the crawler, and launch the MCP server
4. Display the ready-to-use MCP URL

MCP URL: http://localhost:8000/sse

Prerequisites

• Claude Code installed (npm install -g @anthropic-ai/claude-code)
• Docker and Docker Compose V2
• Ports 5432 (PostgreSQL) and 8000 (MCP) available

Persistent Data

PostgreSQL data is stored in ./data/postgres/ on the host machine. This means restarting containers does not lose data — the crawler doesn't need to re-download everything.

Useful Commands

# Watch logs in real-time
docker compose logs -f

# Check service status
docker compose ps

# Stop everything (data preserved)
docker compose down

# Full reset (deletes data)
docker compose down -v && rm -rf ./data/postgres && docker compose up --build -d

MCP Configuration for Claude Desktop

Add to your MCP configuration file:

{
  "mcpServers": {
    "tibiawiki": {
      "url": "http://localhost:8000/sse"
    }
  }
}

Works with Claude Desktop, Claude Code, Cursor, Windsurf, and any MCP-compatible client.

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What It Does

1. Downloads all TibiaWiki pages via the MediaWiki API (raw wikitext)
2. Parses structured infoboxes from 20 entity types (creatures, items, spells, NPCs, quests, etc.)
3. Stores normalized data in PostgreSQL
4. Extracts map coordinates and generates tags/summaries per entity
5. Serves the data via MCP with 19 tools optimized for AI agent queries

Stack

• Python 3.12
• PostgreSQL 16 — relational storage with materialized views
• FastMCP — MCP server for AI agent integration
• Docker + Docker Compose — containerized environment
• psycopg2, requests, python-dotenv
• pgvector + llama-index (optional) — semantic search via embeddings

Project Structure

src/
  main.py           # Orchestrator (runs all 6 phases)
  mcp_server.py     # MCP server with 19 tools
  tagger.py         # Tag and summary generation
  api/              # HTTP client with rate limiting and retry
  parser/           # Parsers by infobox type (20 types)
  db/               # Connection, migrations, and upserts

migrations/         # 28 numbered SQL files
tests/              # pytest + wikitext fixtures
web/                # Next.js landing page

How It Works

src/main.py runs 6 sequential phases:

Phase 0 → Migrations          Apply SQL migrations to the database
Phase 1 → Download            Download wikitext from all pages (batches of 50)
Phase 2 → Parse & Import      Extract infoboxes and upsert into normalized tables
Phase 3 → Positions           Extract {{mapa|X,Y,Z}} coordinates to the positions table
Phase 4 → Tags & Summaries    Generate tags (e.g. "boss", "immune_fire") and text summaries
Phase 5 → Materialized Views  Refresh creature_drops, npc_trades, quest_bosses, etc.

Supported Entities

Entity	Table	Infobox
Creatures	creatures	Infobox_Criatura
Items	items	Infobox_Item
Spells	spells	Infobox_Spell
NPCs	npcs	Infobox_NPC
Quests	quests	Infobox_Quest
Achievements	achievements	Infobox_Achievement
Mounts	mounts	Infobox_Mount
Outfits	outfits	Infobox_Outfit
Imbuements	imbuements	Infobox_Imbuement
Hunts	hunts	Infobox_Hunts
Books	books	Infobox_Book
Buildings	buildings	Infobox_Building
Worlds	worlds	Infobox_World
Runes	runes	Infobox_Runas
World Quests	world_quests	Infobox_World_Quest
World Changes	world_changes	Infobox_World_Change
Familiars	familiars	Infobox Familiar
Tasks	tasks	Infobox_Tasks
Updates	updates	Infobox_Updates
Fansites	fansites	Infobox_Fansite

MCP Server

src/mcp_server.py exposes 19 tools for AI agents to query the database. The suggested usage pattern is:

discover → filter → detail

Tools

Category	Tool	Description
Discovery	describe_tables	Database schema, row counts, and column details
Discovery	list_entities	Browse entities by type with pagination
Search	search	Full-text search across all entity types
Search	search_by_tags	Filter by auto-generated tags
Search	semantic_search	Natural language AI-powered search
Creatures	creature_full_info	Complete profile: stats, loot, hunts, quests
Creatures	creature_weakness	Find creatures weak to a specific element
Creatures	compare_creatures	Side-by-side stat comparison
Items	where_to_get_item	Drops, NPC shops, and quest rewards
Items	where_to_sell_item	NPCs that buy the item and their prices
Items	items_for_vocation	Equipment for a class and body slot
Hunting	recommend_hunt	Best hunts by level and vocation
Hunting	profit_analysis	Estimated gold per kill
Map	get_map_url	Generate TibiaWiki map URLs
Map	search_by_position	Find entities near coordinates
Map	nearby_entities	Entities near a named location
Advanced	rank_entities	Top items by price, strongest creatures, etc.
Advanced	query_database	Custom read-only SQL queries
Advanced	get_entity	Full details for any single entity

Usage Examples

Real questions an AI agent can answer using this MCP:

Equipment recommendation by vocation and level

"What's the recommended set for a Knight level 400?"

The agent uses items_for_vocation("knight") filtering by each slot (helmet, armor, legs, boots, shield, ring) and cross-references with level to build the best combination:

Slot	Item	Armor/Def	Resistances	Skill Boost
Helmet	Spiritthorn Helmet	12 arm	Physical +6%, Energy +10%	Sword/Club/Axe +3
Armor	Spiritthorn Armor	20 arm	Physical +13%	Sword/Club/Axe +4
Legs	Falcon Greaves	10 arm	Physical +7%, Ice +7%	Melee +3
Boots	Pair of Soulwalkers	4 arm	Physical +7%, Fire +5%	Melee +1, Speed +15
Shield	Soulbastion	42 def	Physical +10%, Death +10%	—
Ring	Charged Spiritthorn Ring	2 arm	Physical +8%, All Elements +4%	Melee +3

Complete quest guide

"How do I complete the Desert Quest?"

The agent uses search("desert quest", entity_type="quests") followed by get_entity("quests", "The Desert Dungeon Quest") to return the full spoiler: preparation, required items, step-by-step path, sacrifice room positioning, and rewards.

NPC locations

"Where are the guards for the Inquisition Quest?"

The agent fetches each NPC with get_entity("npcs", "Walter, The Guard") etc., returning exact coordinates and TibiaWiki map links:

NPC	Coordinates	Link
Henricus	32316, 32268, z8	map
Walter, The Guard	32341, 32278, z7	map
Tim, The Guard	32424, 32226, z6	map

More examples

• "Creatures weak to fire?" → creature_weakness("fire")
• "Where does Falcon Longsword drop?" → where_to_get_item("Falcon Longsword")
• "Best hunts for Paladin level 250?" → recommend_hunt(250, "paladin")
• "Most expensive items?" → rank_entities("items", "npc_value")
• "Dragon Lord vs Frost Dragon?" → compare_creatures("Dragon Lord", "Frost Dragon")
• "Where to sell Demon Helmet?" → where_to_sell_item("Demon Helmet")
• "Profit per kill at Hydra?" → profit_analysis("Hydra")

Local Setup (without Docker)

# 1. Copy and edit the configuration file
cp .env.example .env

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run the downloader
python -m src.main

# 4. Start the MCP server
python -m src.mcp_server

Tests

pytest tests/

Fixtures in tests/fixtures/ contain real wikitext samples for testing parsers.

Semantic Search (optional)

To enable semantic search with embeddings:

1. Install the optional dependencies in requirements.txt (uncomment the llama-index lines)
2. Make sure the pgvector extension is enabled in PostgreSQL (migration 027_enable_pgvector.sql)
3. Run python -m src.indexer after the download