How to Use the Weaviate MCP in LlamaIndex

Q: How does LlamaIndex use Weaviate for vector searching?

LlamaIndex takes the output from searchnearvector and incorporates it into a searchable knowledge index. This means you can query based on vectors, but your agent gets answers grounded in actual API data.

Q: Can I use LlamaIndex to inspect the Weaviate schema?

Yes. By running getfullschema and getclassschema, LlamaIndex builds an index of your entire database structure, which improves overall context for the agent.

Q: What is the process for indexing object metadata with Weaviate and LlamaIndex?

You use getobjectdetails to fetch records. LlamaIndex then uses this specific data to create indexed chunks, ensuring that when you ask a question, the source record is cited.

Q: Does Weaviate support listing objects for LlamaIndex?

The listobjects tool supports pagination. LlamaIndex handles this by allowing you to process batches of records and build a comprehensive index from them.

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Connect Weaviate MCP to LlamaIndex

Create your Vinkius account to connect Weaviate 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 Weaviate with LlamaIndex

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Build RAG pipelines using vectors

`search_near_vector` performs the core nearest neighbor search, returning highly relevant documents. The LlamaIndex client takes this output and indexes it into a searchable knowledge base for deep context. This mechanism lets your agent query past API data—like specific object details retrieved via `get_object_details`—and get answers grounded in real records.

Structure the source data index

Before building a knowledge graph, you need to know what's available. Use `get_full_schema` and `get_class_schema`. LlamaIndex processes this structural information, allowing it to build an index of your database capabilities. The resulting metadata is then part of the unified index alongside documents, making all data searchable.

Query specific object records

If you only need info on one record, `get_object_details` pulls it by UUID. LlamaIndex treats this single piece of API data as a key document source. The ability to list objects via `list_objects` lets the client index groups of related records, ensuring all relevant context is available for semantic search.

Setup guide

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

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Common questions about Weaviate MCP in LlamaIndex

LlamaIndex takes the output from `search_near_vector` and incorporates it into a searchable knowledge index. This means you can query based on vectors, but your agent gets answers grounded in actual API data.

Yes. By running `get_full_schema` and `get_class_schema`, LlamaIndex builds an index of your entire database structure, which improves overall context for the agent.

You use `get_object_details` to fetch records. LlamaIndex then uses this specific data to create indexed chunks, ensuring that when you ask a question, the source record is cited.

The `list_objects` tool supports pagination. LlamaIndex handles this by allowing you to process batches of records and build a comprehensive index from them.

This server deals with vector floats, UUIDs, schema strings, and object metadata used to ground the resulting knowledge base.

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