The Opera Omnia MCP server provides programmatic access to the rich collection of JSON datasets from the Opera Omnia project. It enables developers, storytellers, and AI applications to easily access, combine, and generate creative content for games, interactive fiction, chatbots, and more.
What is triptych opera omnia mcp
Opera Omnia MCP Server
An MCP server that provides access to the rich collection of JSON datasets from the Opera Omnia project, a comprehensive library of creative content for games, storytelling, and bot development.
Features
- Access to all Opera Omnia datasets
- Random selection from datasets
- Filtering datasets by criteria
- Combining multiple datasets
- Generating creative content using templates
Installation
- Clone this repository
- Install dependencies:
npm install
- Build the project:
npm run build
Usage
Running the Server
npm start
MCP Configuration
Add the following to your MCP settings file:
{
"mcpServers": {
"opera-omnia": {
"command": "node",
"args": ["path/to/opera-omnia-mcp/build/index.js"],
"disabled": false,
"autoApprove": []
}
}
}
Replace path/to/opera-omnia-mcp
with the actual path to this project.
Available Tools
list_categories
List all available data categories.
const result = await use_mcp_tool({
server_name: "opera-omnia",
tool_name: "list_categories",
arguments: {}
});
list_datasets
List all datasets within a category.
const result = await use_mcp_tool({
server_name: "opera-omnia",
tool_name: "list_datasets",
arguments: {
category: "characters"
}
});
get_dataset
Get the complete contents of a specific dataset.
const result = await use_mcp_tool({
server_name: "opera-omnia",
tool_name: "get_dataset",
arguments: {
category: "characters",
dataset: "personalities"
}
});
get_random_item
Get a random item from a specific dataset.
const result = await use_mcp_tool({
server_name: "opera-omnia",
tool_name: "get_random_item",
arguments: {
category: "characters",
dataset: "personalities"
}
});
get_filtered_items
Get items from a dataset that match specific criteria.
const result = await use_mcp_tool({
server_name: "opera-omnia",
tool_name: "get_filtered_items",
arguments: {
category: "characters",
dataset: "personalities",
filter: "brave"
}
});
combine_datasets
Combine multiple datasets and get random selections.
const result = await use_mcp_tool({
server_name: "opera-omnia",
tool_name: "combine_datasets",
arguments: {
datasets: [
{ category: "characters", dataset: "personalities" },
{ category: "characters", dataset: "backstories" }
],
count: 3
}
});
generate_content
Generate creative content based on multiple datasets.
const result = await use_mcp_tool({
server_name: "opera-omnia",
tool_name: "generate_content",
arguments: {
template: "A {adjective} {class} must {quest} to obtain {artifact}",
datasets: {
adjective: { category: "attributes", dataset: "adjectives" },
class: { category: "rpg", dataset: "classes" },
quest: { category: "situations", dataset: "quests" },
artifact: { category: "equipment", dataset: "artifacts" }
}
}
});
Available Resources
opera-omnia://categories
List of all available data categories.
const result = await access_mcp_resource({
server_name: "opera-omnia",
uri: "opera-omnia://categories"
});
opera-omnia://category/{category}
List of datasets available in a specific category.
const result = await access_mcp_resource({
server_name: "opera-omnia",
uri: "opera-omnia://category/characters"
});
opera-omnia://dataset/{category}/{dataset}
Contents of a specific dataset.
const result = await access_mcp_resource({
server_name: "opera-omnia",
uri: "opera-omnia://dataset/characters/personalities"
});
Future Enhancements
We have several ideas for future enhancements to the Opera Omnia MCP server:
-
Advanced Content Generation: Add more sophisticated content generation capabilities beyond simple template substitution.
-
Improved Caching: Implement better caching mechanisms for improved performance, especially for frequently accessed datasets.
-
User-Contributed Datasets: Add support for user-contributed datasets, allowing users to extend the available content.
-
Visualization Tools: Create visualization tools for exploring the data and understanding relationships between different datasets.
-
Local Data Files: Add support for local data files as an alternative to fetching from GitHub.
-
Integration Examples: Provide more examples of integrating the MCP server with different applications and frameworks.
Release Notes
For detailed information about the current and past releases, see the RELEASE_NOTES.md file.
License
This project is licensed under the MIT License - see the LICENSE.md file for details.
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Frequently Asked Questions
What is MCP?
MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.
What are MCP Servers?
MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.
How do MCP Servers work?
MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.
Are MCP Servers secure?
Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.
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