Can my AI agent do a semantic search across my vector database?

Yes! Provided you supply the embedded query vector, your agent can issue a `vector search` command to the Vald Engine. It will rapidly scan millions of indexes natively using its ANN algorithms and return the top-K closest neighbors associated with your data.

How do I ensure my Vald cluster is healthy right from my CLI?

Skip complex diagnostics loops. Instruct your agent to `get Vald internal engine info`. It will interface directly via gRPC/REST and pull down cluster metrics including operational status, agent versions, and basic diagnostic health. This is vital for MLOps managing production RAG pipelines needing constant reassurance.

Can I permanently purge a corrupted vector embedding?

When a document becomes stale in your knowledge base, you must remove its embedding. Ask the AI agent: `permanently delete vector ID 'doc-xyz'`. Using the `removeVector` capability, it targets your cluster and ensures the outdated semantic representation is fully expunged without risking other node data.

Vald MCP Server for LlamaIndex 6 tools — connect in under 2 minutes

Name: Vald
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Vald as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Vald. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Vald?"
    )
    print(response)

asyncio.run(main())

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About Vald MCP Server

Connect your Vald cluster to any AI agent and bring distributed, high-speed approximate nearest neighbor (ANN) vector search directly to your conversational workflow.

LlamaIndex agents combine Vald tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the 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

Vector Search — Perform rapid semantic searches across millions of embedded data points just by querying the agent.
Data Ingestion — Insert new high-dimensional vectors directly into the Vald index for instant future retrievability in your RAG pipelines.
Index Management — Update the vector representations of existing records or permanently remove specific items from the engine cluster.
Cluster Health — Automatically retrieve operational system information, agent health statuses, and node details regarding your active Vald deployment.

The Vald MCP Server exposes 6 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 Vald to LlamaIndex via MCP

Follow these steps to integrate the Vald 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 6 tools from Vald

Why Use LlamaIndex with the Vald MCP Server

LlamaIndex provides unique advantages when paired with Vald through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Vald tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Vald tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Vald, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Vald tools were called, what data was returned, and how it influenced the final answer

Vald + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Vald MCP Server delivers measurable value.

Hybrid search: combine Vald real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Vald 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 Vald for fresh data

Analytical workflows: chain Vald queries with LlamaIndex's data connectors to build multi-source analytical reports

Vald MCP Tools for LlamaIndex (6)

These 6 tools become available when you connect Vald to LlamaIndex via MCP:

delete_vector

This action is irreversible. Permanently removes a vector from the Vald index

get_engine_info

Retrieves operational information and health of the Vald engine

get_vector_details

Retrieves the raw vector data for a specific ID

insert_vector

Provide a unique ID and the vector as a JSON array. Inserts a new vector into the Vald index

search_vectors

Provide a query vector as a JSON array of floats. Performs a nearest neighbor vector similarity search

update_vector

Provide the existing ID and new vector array. Updates an existing vector in the Vald index

Example Prompts for Vald in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Vald immediately.

"Is the Vald cluster operational right now?"

"Can you check the vector details stored for UUID 'user-profile-89'?"

"Update the existing item 'context-fragment-12' with this new 1536-dimensional array: [0.38, -0.19, 0...]."

Troubleshooting Vald MCP Server with LlamaIndex

Common issues when connecting Vald to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Vald + LlamaIndex FAQ

Common questions about integrating Vald MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.