What is the format required for the 'Redis URL' parameter?

The parameter requires standard Redis URI string formatting. Typically it looks like `redis://[username]:[password]@[host]:[port]`. For TLS/SSL-enabled endpoints spanning secure setups, use the `rediss://` scheme prefix.

Does my Redis instance strictly need the RediSearch module?

Yes, absolutely. The base Redis product (standard open-source) only manages key-value caching out of the box. You must be running the Redis Stack or a managed tier (like Redis Enterprise or compatible cloud offerings) that explicitly includes RediSearch to generate and query KNN vector indexes.

Can I query using embedding arrays output directly from OpenAI models?

Yes. Once you receive your numerical float array from an embedding model (like text-embedding-ada-002), you can pipe that exact JSON array into the `search_vectors` agent tool alongside the relevant index name to perform immediate proximity lookups.

Redis Vector MCP Server for Windsurf 6 tools — connect in under 2 minutes

Name: Redis Vector
Availability: InStock
Author: Vinkius

docs.windsurf.com/windsurf/mcp

Built by Vinkius GDPR 6 Tools IDE

Windsurf brings agentic AI coding to a purpose-built IDE. Connect Redis Vector through Vinkius and Cascade will auto-discover every tool. ask questions, generate code, and act on live data without leaving your editor.

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Classic Setup·json

{
  "mcpServers": {
    "redis-vector": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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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 Redis Vector MCP Server

Connect your Redis database (equipped with the RediSearch module) to your AI agent, turning it into an advanced Vector Database administrator. Activating this integration grants your conversational interface the power to interact directly with your semantic search engine, enabling tasks like querying mathematical embeddings for similar records, configuring fresh vector indexes, and managing geometric data structures without needing dedicated external database clients.

Windsurf's Cascade agent chains multiple Redis Vector tool calls autonomously. query data, analyze results, and generate code in a single agentic session. Paste Vinkius Edge URL, reload, and all 6 tools are immediately available. Real-time tool feedback appears inline, so you see API responses directly in your editor.

What you can do

Similarity Vector Search (KNN) — Let the AI perform rapid native vector comparisons (search_vectors). Provide an embedding array via prompt or code, and retrieve the absolute nearest top_k neighbors securely cached in your infrastructure.
Index Management — Actively discover all loaded RediSearch vector indexes, investigate their configured dimensions (get_index_info), or command the AI to instantiate new KNN indexes (create_vector_index) tailored for fresh AI workloads.
Embedding Administration — Inject and modify geometric vector components associated with a document key (upsert_vector), or purge legacy embeddings efficiently (delete_vector) to keep semantic records clean and operational.

The Redis Vector MCP Server exposes 6 tools through the Vinkius. Connect it to Windsurf 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 Redis Vector to Windsurf via MCP

Follow these steps to integrate the Redis Vector MCP Server with Windsurf.

Open MCP Settings

Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"

Add the server

Paste the JSON configuration above into mcp_config.json

Save and reload

Windsurf will detect the new server automatically

Start using Redis Vector

Open Cascade and ask: "Using Redis Vector, help me...". 6 tools available

Why Use Windsurf with the Redis Vector MCP Server

Windsurf provides unique advantages when paired with Redis Vector through the Model Context Protocol.

Windsurf's Cascade agent autonomously chains multiple tool calls in sequence, solving complex multi-step tasks without manual intervention

Purpose-built for agentic workflows. Cascade understands context across your entire codebase and integrates MCP tools natively

JSON-based configuration means zero code changes: paste a URL, reload, and all 6 tools are immediately available

Real-time tool feedback is displayed inline, so you see API responses directly in your editor without switching contexts

Redis Vector + Windsurf Use Cases

Practical scenarios where Windsurf combined with the Redis Vector MCP Server delivers measurable value.

Automated code generation: ask Cascade to fetch data from Redis Vector and generate models, types, or handlers based on real API responses

Live debugging: query Redis Vector tools mid-session to inspect production data while debugging without leaving the editor

Documentation generation: pull schema information from Redis Vector and have Cascade generate comprehensive API docs automatically

Rapid prototyping: combine Redis Vector data with Cascade's code generation to scaffold entire features in minutes

Redis Vector MCP Tools for Windsurf (6)

These 6 tools become available when you connect Redis Vector to Windsurf via MCP:

create_vector_index

Specify the name and vector dimensions. Creates a new RediSearch vector index

delete_vector

Deletes a vector document from Redis

get_index_info

Retrieves details for a specific vector index

list_indexes

Lists all RediSearch vector indexes

search_vectors

Provide the query vector as a JSON array of floats. Performs a KNN similarity search in a vector index

upsert_vector

Specify the document key and the vector as a JSON array. Inserts or updates a vector in a Redis hash

Example Prompts for Redis Vector in Windsurf

Ready-to-use prompts you can give your Windsurf agent to start working with Redis Vector immediately.

"Search the index 'customer-support-vector' for the top 3 similar records to this embedding vector: [0.12, -0.45, 0.08, 0.99...]"

"Insert a new embedding into the database with the key 'user:439:preference' containing the vector `[0.2, -0.1...]`."

"Retrieve the index information logic and schema mapping for 'docs-semantic-index'."

Troubleshooting Redis Vector MCP Server with Windsurf

Common issues when connecting Redis Vector to Windsurf through the Vinkius, and how to resolve them.

Server not connecting

Check Settings → MCP for the server status. Try toggling it off and on.

Redis Vector + Windsurf FAQ

Common questions about integrating Redis Vector MCP Server with Windsurf.

How does Windsurf discover MCP tools?

Windsurf reads the mcp_config.json file on startup and connects to each configured server via Streamable HTTP. Tools are listed in the MCP panel and available to Cascade automatically.

Can Cascade chain multiple MCP tool calls?

Yes. Cascade is an agentic system. it can plan and execute multi-step workflows, calling several tools in sequence to accomplish complex tasks without manual prompting between steps.

Does Windsurf support multiple MCP servers?

Yes. Add as many servers as needed in mcp_config.json. Each server's tools appear in the MCP panel and Cascade can use tools from different servers in a single flow.