Vald

Vald MCP Connector for Claude

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Power your agent with Vald — query, insert, and manage dense vectors on a highly scalable, distributed nearest-neighbor engine.

6 tools Official Updated Jun 28, 2026 Official Vinkius Partner

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

  1. Subscribe to this server
  2. Enter your Vald Gateway Host address
  3. 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.
vector-searchanndistributed-systemsmachine-learninghigh-performanceembeddings

6 tools expose this connector's capabilities to your AI agent.

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

delete_vector

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

update_vector

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

search_vectors

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

See how to talk to your AI agent using Vald.

Is the Vald cluster operational right now?

I've checked the Vald Gateway. The engine is healthy and accepting requests! The current clustered infrastructure responds well to Ping mechanisms.

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

I retrieved the vector data for `user-profile-89`. The embedding array spans 768 dimensions. Do you want me to render its raw float values here or proceed to query for its nearest neighbors?

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

Done. The vector for ID `context-fragment-12` has been successfully updated in your Vald cluster. The engine replaced its old representation with your newly uploaded 1536-dimensional array without breaking any active query connections.

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.

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