rct.ai MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add rct.ai as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to rct.ai. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in rct.ai?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About rct.ai MCP Server
Connect your AI agents to rct.ai, the advanced narrative engine for the gaming and metaverse industry. This MCP provides 10 tools to orchestrate autonomous virtual beings using the Morpheus Cloud and the Chaos Box algorithm.
LlamaIndex agents combine rct.ai tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- NPC Interaction — Send user input to NPCs and receive real-time dialogue and behavioral decisions
- Character Lifecycle — Create, update, and inspect AI character profiles and personalities
- Contextual Sessions — Manage persistent interaction sessions between players and virtual beings
- Narrative Logic — Configure the Chaos Box to balance stochastic randomness and scripted narrative flow
The rct.ai MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect rct.ai to LlamaIndex via MCP
Follow these steps to integrate the rct.ai MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from rct.ai
Why Use LlamaIndex with the rct.ai MCP Server
LlamaIndex provides unique advantages when paired with rct.ai through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine rct.ai tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain rct.ai tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query rct.ai, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what rct.ai tools were called, what data was returned, and how it influenced the final answer
rct.ai + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the rct.ai MCP Server delivers measurable value.
Hybrid search: combine rct.ai real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query rct.ai to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying rct.ai for fresh data
Analytical workflows: chain rct.ai queries with LlamaIndex's data connectors to build multi-source analytical reports
rct.ai MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect rct.ai to LlamaIndex via MCP:
create_character
Create a new AI NPC profile
create_session
Initialize a new interaction session
get_chaos_box_config
Retrieve current Chaos Box logic parameters
get_character
Get detailed configuration of a specific AI character
get_session
Retrieve details about an ongoing interaction session
list_assets
List assets or knowledge bases attached to characters
list_characters
List all AI characters managed in your account
send_inference
Send user input to an AI NPC and get a response
update_chaos_box_config
Update Chaos Box decision logic parameters
update_character
Update an existing AI character configuration
Example Prompts for rct.ai in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with rct.ai immediately.
"Send interaction message 'Who are you?' to NPC character 'char_123'."
"List all AI characters managed in my account."
"Get the current Chaos Box configuration."
Troubleshooting rct.ai MCP Server with LlamaIndex
Common issues when connecting rct.ai to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcprct.ai + LlamaIndex FAQ
Common questions about integrating rct.ai MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect rct.ai with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect rct.ai to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.