LoreKeeper MCP

LoreKeeper MCP

Provides fast, cached access to comprehensive Dungeons & Dragons 5th Edition data including spells, monsters, classes, races, equipment, and rules through Open5e and D\&D 5e APIs.

Category
访问服务器

README

LoreKeeper MCP

A Model Context Protocol (MCP) server for D&D 5e information lookup with AI assistants. LoreKeeper provides fast, cached access to comprehensive Dungeons & Dragons 5th Edition data through the Open5e and D&D 5e APIs.

Features

  • Comprehensive D&D 5e Data: Access spells, monsters, classes, races, equipment, and rules
  • Intelligent Caching: SQLite-based caching with TTL support for fast responses
  • Dual API Support: Primary Open5e API with D&D 5e API fallback for complete coverage
  • Type-Safe Configuration: Pydantic-based configuration management
  • Modern Python Stack: Built with Python 3.13+, async/await patterns, and FastMCP
  • Production Ready: Comprehensive test suite, code quality tools, and pre-commit hooks

Quick Start

Prerequisites

  • Python 3.13 or higher
  • uv for package management

Installation

# Clone the repository
git clone https://github.com/your-org/lorekeeper-mcp.git
cd lorekeeper-mcp

# Install dependencies
uv sync

# Set up pre-commit hooks
uv run pre-commit install

# Copy environment configuration
cp .env.example .env

Running the Server

# Start the MCP server
uv run python -m lorekeeper_mcp

# Or with custom configuration
LOG_LEVEL=DEBUG uv run python -m lorekeeper_mcp

Available Tools

LoreKeeper provides 5 MCP tools for querying D&D 5e game data:

  1. lookup_spell - Search spells by name, level, school, class, and properties
  2. lookup_creature - Find monsters by name, CR, type, and size
  3. lookup_character_option - Get classes, races, backgrounds, and feats
  4. lookup_equipment - Search weapons, armor, and magic items
  5. lookup_rule - Look up game rules, conditions, and reference information

See docs/tools.md for detailed usage and examples.

Configuration

LoreKeeper uses environment variables for configuration. Create a .env file:

# Database settings
DB_PATH=./data/cache.db
CACHE_TTL_DAYS=7
ERROR_CACHE_TTL_SECONDS=300

# Logging
LOG_LEVEL=INFO
DEBUG=false

# API endpoints
OPEN5E_BASE_URL=https://api.open5e.com
DND5E_BASE_URL=https://www.dnd5eapi.co/api

Development

Project Structure

lorekeeper-mcp/
├── src/lorekeeper_mcp/          # Main package
│   ├── cache/                   # Database caching layer
│   │   └── db.py               # SQLite cache implementation
│   ├── api_clients/            # External API clients
│   ├── tools/                  # MCP tool implementations
│   ├── config.py               # Configuration management
│   ├── server.py               # FastMCP server setup
│   └── __main__.py            # Package entry point
├── tests/                      # Test suite
│   ├── test_cache/            # Cache layer tests
│   ├── test_config.py         # Configuration tests
│   ├── test_server.py         # Server tests
│   └── conftest.py            # Pytest fixtures
├── docs/                       # Documentation
├── pyproject.toml             # Project configuration
├── .pre-commit-config.yaml    # Code quality hooks
└── README.md                  # This file

Running Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=lorekeeper_mcp

# Run specific test file
uv run pytest tests/test_cache/test_db.py

Code Quality

The project uses several code quality tools:

  • Black: Code formatting (100 character line length)
  • Ruff: Linting and import sorting
  • MyPy: Static type checking
  • Pre-commit: Git hooks for automated checks
# Run all quality checks
uv run ruff check src/
uv run ruff format src/
uv run mypy src/

# Run pre-commit hooks manually
uv run pre-commit run --all-files

Database Cache

LoreKeeper uses SQLite with WAL mode for efficient caching:

  • Schema: api_cache table with indexes on expiration and content type
  • TTL Support: Configurable cache duration (default: 7 days)
  • Content Types: Spells, monsters, equipment, etc. for organized storage
  • Source Tracking: Records which API provided cached data
  • Automatic Cleanup: Expired entries are automatically pruned

API Strategy

The project follows a strategic API assignment:

  1. Prefer Open5e API over D&D 5e API
  2. Prefer Open5e v2 over v1 when available
  3. Use D&D 5e API only for content not available in Open5e (primarily rules)
  4. Each category maps to ONE API to avoid complexity

See docs/tools.md for detailed API mapping and implementation notes.

📋 OpenSpec Integration

This project uses OpenSpec as its core development tooling for specification management and change tracking. OpenSpec provides:

  • Structured Specifications: All features, APIs, and architectural changes are documented in detailed specs
  • Change Management: Comprehensive change tracking with proposals, designs, and implementation tasks
  • Living Documentation: Specifications evolve alongside the codebase, ensuring documentation stays current
  • Development Workflow: Integration between specs, implementation, and testing

The openspec/ directory contains:

  • Current specifications for all project components
  • Historical change records with full context
  • Design documents and implementation plans
  • Task breakdowns for development work

When contributing, please review relevant specifications in openspec/ and follow the established change management process.

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Workflow

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature-name
  3. Make your changes and ensure tests pass
  4. Run code quality checks: uv run pre-commit run --all-files
  5. Commit your changes
  6. Push to your fork and create a pull request

Testing

All contributions must include tests:

  • New features should have corresponding unit tests
  • Maintain test coverage above 90%
  • Use pytest fixtures for consistent test setup
  • Follow async/await patterns for async code

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选