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 Using the `removeVector` capability, it targets your cluster and ensures the outdated semantic representation is fully expunged without risking other node data.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 Pydantic AI 6 tools — connect in under 2 minutes

Name: Vald
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Vald through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Vald "
            "(6 tools)."
        ),
    )

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

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

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.

Pydantic AI validates every Vald tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Vald MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Vald MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Vald integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Vald connection logic from agent behavior for testable, maintainable code

Vald + Pydantic AI Use Cases

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

Type-safe data pipelines: query Vald with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Vald tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Vald and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Vald responses and write comprehensive agent tests

Vald MCP Tools for Pydantic AI (6)

These 6 tools become available when you connect Vald to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Vald + Pydantic AI FAQ

Common questions about integrating Vald MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Vald MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.