Bring Vector Search
to Pydantic AI
Create your Vinkius account to connect Vald to Pydantic AI and start using all 6 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the 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.
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.
How it works
- Subscribe to this server
- Enter your Vald Gateway Host address
- Start performing semantic queries and updates from Claude, Cursor, or any MCP-compatible client
Your AI agent becomes the direct line to your massive vector knowledge base.
Who is this for?
- Machine Learning Engineers — rapidly test and visualize embedding changes against a live Vald instance without scripting.
- Data Scientists — execute on-the-fly 'top-k' semantic queries directly from an IDE to validate search recall results.
- DevOps Engineers — check the active engine health status and cluster info via natural language whenever anomalies happen.
- Backend Developers — quickly purge corrupted vectors or update legacy records bypassing native database terminals.
Built-in capabilities (6)
This action is irreversible. Permanently removes a vector from the Vald index
Retrieves operational information and health of the Vald engine
Retrieves the raw vector data for a specific ID
Provide a unique ID and the vector as a JSON array. Inserts a new vector into the Vald index
Provide a query vector as a JSON array of floats. Performs a nearest neighbor vector similarity search
Provide the existing ID and new vector array. Updates an existing vector in the Vald index
Why Pydantic AI?
Pydantic AI validates every Vald tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through 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.
- —
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Vald integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your Vald connection logic from agent behavior for testable, maintainable code
Vald in Pydantic AI
Why run Vald with Vinkius?
The Vald connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 6 tools are ready to work instantly without any complex setup.
You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.
* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure
Over 4,000 integrations ready for AI agents
Explore a vast library of pre-built integrations, optimized and ready to deploy.
Connect securely in under 30 seconds
Generate tokens to authenticate and link external services in a single step.
Complete visibility into every agent action
Audit live requests, latency, success rates, and active security compliance policies.
Optimize spending and track token ROI
Analyze real-time token consumption and cost metrics detailed by connection.
Explore our live AI Agents Analytics dashboard to see it all working
This dashboard is included when you connect Vald using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.
Vald and 4,000+ other AI tools. No hosting, no code, ready to use.
Professionals who connect Vald to Pydantic AI through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.
Raw MCP | Vinkius | |
|---|---|---|
| Ready-to-use MCPs | Find and configure each manually | 4,000+ MCPs ready to use |
| Connection Setup | Manual coding & server setup | 1-click instant connection |
| Server Hosting | You host it yourself (needs 24/7 uptime) | 100% hosted & managed by Vinkius |
| Security & Privacy | Stored in plaintext config files | Bank-grade encrypted vault |
| Activity Visibility | Blind execution (no logs or tracking) | Live dashboard with real-time logs |
| Cost Control | Runaway AI token spend risk | Automatic budget limits |
| Revoking Access | Must delete files or code to stop | 1-click disconnect button |
How Vinkius secures
Vald for Pydantic AI
Every request between Pydantic AI and Vald is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.
Frequently asked questions
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.
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.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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