How to Use the Ada MCP in LlamaIndex

Q: Can LlamaIndex update my Ada knowledge base?

Your agent writes new documentation using the createarticle tool. It formats the title and text content based on gaps it finds in your current vector store.

Q: How does LlamaIndex handle Ada user profiles?

The agent calls getenduser to fetch specific profile attributes. It then injects those metavariables into the prompt context before generating a response.

Index your Ada customer support conversations into searchable LlamaIndex vector stores.

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Connect Ada MCP to LlamaIndex

Create your Vinkius account to connect Ada 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 Ada with LlamaIndex

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Index Ada articles for semantic search

The `list_articles` tool pulls your complete help catalog so LlamaIndex can embed the text into a vector store. Your RAG application queries this index to find exact matches for customer issues instead of guessing. You combine these live API documents with internal PDFs to build a unified knowledge base. The function agent cites the specific Ada article ID when answering employee questions.

Ground answers in support chats

Running `list_conversations` extracts past interactions handled by the Ada bot. LlamaIndex ingests these raw transcripts and turns them into queryable nodes. Your agent answers questions based on actual resolution paths rather than fabricated examples. When a manager asks how a specific bug was handled, the query engine searches the embedded chat history to find the exact moment the issue was resolved.

LlamaIndex MCP Server integration

The `get_end_user` tool retrieves profile metavariables directly through the McpToolSpec wrapper connecting to the MCP Server. You pass the End User ID, and the agent fetches the exact subscription tier or location data. Filtering data access is simple using the allowed_tools parameter. You restrict the function agent so it only reads user profiles without having permission to write new content via create_article.

Setup guide

Set up Ada 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 Ada 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 Ada tools.",
)
response = await agent.run("List recent Ada data")

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Common questions about Ada MCP in LlamaIndex

Install llama-index-tools-mcp via pip. Initialize a BasicMCPClient with your Vinkius endpoint, wrap it in McpToolSpec, and call to_tool_list_async() to pass the functions to your agent.

Your agent writes new documentation using the create_article tool. It formats the title and text content based on gaps it finds in your current vector store.

The agent calls get_end_user to fetch specific profile attributes. It then injects those metavariables into the prompt context before generating a response.

You pass an allowed list to the tool spec constructor. This prevents the agent from calling write operations if you only want it to read conversation data.

Support conversations and profile metavariables route through an ephemeral V8 sandbox. Authentication requires just one endpoint token, and the managed infrastructure retains absolutely zero data after the MCP request completes.

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