OpenSearch Vector MCP Server for Claude Code 6 tools — connect in under 2 minutes
Claude Code is Anthropic's agentic CLI for terminal-first development. Add OpenSearch Vector as an MCP server in one command and Claude Code will discover every tool at runtime — ideal for automation pipelines, CI/CD integration, and headless workflows via the Vinkius.
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# Your Vinkius token — get it at cloud.vinkius.com
claude mcp add opensearch-vector --transport http "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
* 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 OpenSearch Vector MCP Server
Turn your OpenSearch cluster into an AI-native vector database. Create k-NN indexes, upsert embeddings, run similarity searches, and inspect index configurations — all through natural conversation with your AI agent.
Claude Code registers OpenSearch Vector as an MCP server in a single terminal command. Once connected, Claude Code discovers all 6 tools at runtime and can call them headlessly — ideal for CI/CD pipelines, cron jobs, and automated workflows where OpenSearch Vector data drives decisions without human intervention.
What you can do
- Vector Search — Execute k-Nearest Neighbors queries against any k-NN index with custom top-K limits and dense float vectors
- Index Management — List all cluster indexes with health status and document counts, or inspect a specific index's vector dimension, engine config, and distance metric
- Create Index — Provision new k-NN indexes optimized for cosine similarity with configurable vector dimensions (384, 768, 1536, etc.)
- Document Operations — Upsert vector documents with metadata, or delete documents from the embedding space by ID
The OpenSearch Vector MCP Server exposes 6 tools through the Vinkius. Connect it to Claude Code 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 OpenSearch Vector to Claude Code via MCP
Follow these steps to integrate the OpenSearch Vector MCP Server with Claude Code.
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
Add the MCP Server
Run the command above in your terminal
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
Start using OpenSearch Vector
Ask Claude: "Using OpenSearch Vector, show me..." — 6 tools are ready
Why Use Claude Code with the OpenSearch Vector MCP Server
Claude Code provides unique advantages when paired with OpenSearch Vector through the Model Context Protocol.
Single-command setup: `claude mcp add` registers the server instantly — no config files to edit or applications to restart
Terminal-native workflow means MCP tools integrate seamlessly into shell scripts, CI/CD pipelines, and automated DevOps tasks
Claude Code runs headlessly, enabling unattended batch processing using OpenSearch Vector tools in cron jobs or deployment scripts
Built by the same team that created the MCP protocol, ensuring first-class compatibility and the fastest adoption of new protocol features
OpenSearch Vector + Claude Code Use Cases
Practical scenarios where Claude Code combined with the OpenSearch Vector MCP Server delivers measurable value.
CI/CD integration: embed OpenSearch Vector tool calls in your deployment pipeline to validate configurations or fetch secrets before shipping
Headless batch processing: schedule Claude Code to query OpenSearch Vector nightly and generate reports without human intervention
Shell scripting: pipe OpenSearch Vector outputs into other CLI tools for data transformation, filtering, and aggregation
Infrastructure monitoring: run Claude Code in a cron job to query OpenSearch Vector status endpoints and alert on anomalies
OpenSearch Vector MCP Tools for Claude Code (6)
These 6 tools become available when you connect OpenSearch Vector to Claude Code via MCP:
create_index
knn: true` and mapping a rigid dynamic dense vector field optimized for cosine similarity. Create a new native OpenSearch KNN index ready for vector embeddings
delete_document
Delete an explicit vector document bounding from OpenSearch
get_index
Retrieve explicit OpenSearch index mapping and settings
index_document
This executes a fast transactional atomic insertion into the embedding space. Upsert a singular vector document directly into an OpenSearch KNN index
list_indexes
List all explicit indexes residing on the OpenSearch cluster
search
Provide the exact index name and a JSON-stringified dense float vector array to find conceptually similar embeddings natively. Execute a K-Nearest Neighbors (k-NN) vector search against OpenSearch
Example Prompts for OpenSearch Vector in Claude Code
Ready-to-use prompts you can give your Claude Code agent to start working with OpenSearch Vector immediately.
"List all vector indexes in my OpenSearch cluster."
"Find the 5 most similar documents to this embedding in the knowledge-base index."
"Create a new k-NN index called 'customer-feedback' with 1536 dimensions."
Troubleshooting OpenSearch Vector MCP Server with Claude Code
Common issues when connecting OpenSearch Vector to Claude Code through the Vinkius, and how to resolve them.
Command not found: claude
npm install -g @anthropic-ai/claude-codeConnection timeout
OpenSearch Vector + Claude Code FAQ
Common questions about integrating OpenSearch Vector MCP Server with Claude Code.
How do I add an MCP server to Claude Code?
claude mcp add --transport http "" in your terminal. Claude Code registers the server and discovers all tools immediately.Can Claude Code run MCP tools in headless mode?
How do I list all connected MCP servers?
claude mcp in your terminal to see all registered servers and their status, or type /mcp inside an active Claude Code session.Connect OpenSearch Vector with your favorite client
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Connect OpenSearch Vector to Claude Code
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.
