How to Use the Convai MCP in LlamaIndex

Index Convai character data into LlamaIndex to build RAG apps that ground character responses in your specific API data.

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Connect Convai MCP to LlamaIndex

Create your Vinkius account to connect Convai to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Convai with LlamaIndex

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Claude

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Ground responses with LlamaIndex

Use `get_chat_session_details` to pull historical transcripts and index them locally. This lets your LlamaIndex setup find patterns in past conversations for better context. Your agent builds a searchable knowledge base from this output. It stops hallucinations by grounding every answer in the actual API data.

Automate character setup in LlamaIndex

Call `list_characters` and pass the results to your indexer. You can now query your entire character roster using natural language. This makes managing dozens of personas easy. You just search your index to find the right ID for `get_prompt` or `update_character` calls.

Search narrative triggers

Extract `list_narrative_triggers` into your vector store. Your indexer makes these triggers queryable alongside your other project documents. When a specific event happens, your agent finds the relevant trigger ID quickly. It keeps your game logic mapped to your live data.

Setup guide

Set up Convai MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Convai MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Convai tools.",
)
response = await agent.run("List recent Convai data")

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Common questions about Convai MCP in LlamaIndex

Use the MCP client spec to convert tools into a format LlamaIndex understands. Once converted, pass them to your FunctionAgent to start querying your data.

Absolutely. You can index character profiles and chat history to create a unified search interface for all your Convai assets.

Vinkius isolates your server connection in a sandbox. Only your authenticated client can access the tools that read or write your character configurations.

By indexing the results from the tools, you force the agent to use actual data. It relies on the vector store rather than guessing.

Chat session logs reside on the Convai platform. This server provides a secure gateway to fetch those logs for your indexing pipeline.

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