How to Use the Wolai MCP in LlamaIndex

Q: Can LlamaIndex search across different Wolai databases?

Yes. You first call listdatabases to find all available sources, then you query specific ones using querydatabase, and the results are all indexed for later retrieval.

Q: How does LlamaIndex handle page blocks?

You use listblocks to see the structure of a Wolai page. This detailed output is then chunked and indexed, letting your agent search down to the specific block level.

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Connect Wolai MCP to LlamaIndex

Create your Vinkius account to connect Wolai 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 Wolai with LlamaIndex

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MCP Server: Indexed Knowledge Retrieval

When you use `query_database`, the result isn't just an answer; it becomes part of your searchable knowledge base. LlamaIndex takes that output and indexes it, allowing you to query past database records semantically later. This means your application can answer questions like, 'What did we find about Project X in Q3?' by retrieving the raw `query_database` data points.

Structured Data Querying

The tool `get_page` pulls detailed Wolai page content. LlamaIndex then chunks and indexes that text, making every piece of information searchable by meaning, not just keywords. Developers build RAG apps where live API data—like the output from `get_workspace_info`—is combined into one unified index.

Listing and Context Building

The ability to call tools like `list_pages` or `list_databases` is crucial for context. LlamaIndex indexes the *metadata* of these lists, so you can ask questions about your organization's structure itself. Example: 'How many pages exist in the Marketing department?'—the index uses data from `list_pages` to answer.

Setup guide

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

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Common questions about Wolai MCP in LlamaIndex

Yes. You first call `list_databases` to find all available sources, then you query specific ones using `query_database`, and the results are all indexed for later retrieval.

You use `list_blocks` to see the structure of a Wolai page. This detailed output is then chunked and indexed, letting your agent search down to the specific block level.

The MCP Server allows you to query current states using tools like `get_database` or `list_users`. These results are indexed, creating a historical record for semantic search.

The Wolai MCP Server touches database schemas, user lists, and page content. All data types can be incorporated into your vector store.

You run the query against the indexed results from tools like `get_database` and `query_database`. The index finds the answer grounded in actual API data.

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