Weaviate MCP Server: Setup, Features & Best Alternatives (2026)
Does Weaviate Have an MCP Server?
As of February 2026, Weaviate does not offer an official, first-party MCP server. This is the current landscape:
- Official MCP server: Not available from Weaviate directly
- Community wrappers: Check GitHub for community-maintained projects that wrap the Weaviate API for MCP compatibility
- API availability: Weaviate offers a REST API that could be wrapped in a custom MCP server
- Expected timeline: No public announcements from Weaviate about MCP support
The absence of an official server doesn’t mean you can’t use Weaviate data with AI assistants. Several approaches exist, ranging from community wrappers to building your own MCP server around the Weaviate API.
Why Connect Weaviate to an MCP Server?
Weaviate falls into the Databases & Data Infrastructure category of tools, providing database queries, data pipeline management, and analytics infrastructure. Data engineers and analysts spend significant time writing queries and managing pipelines across different tools. An MCP server brings database access into your AI workflow, letting you query, analyze, and transform data conversationally.
Practical Use Cases
- Run database queries using natural language through your AI assistant
- Explore table schemas and relationships without a separate client
- Generate and optimize SQL queries with AI assistance and live execution
- Monitor data pipeline health and troubleshoot failures from your terminal
- Build data transformation logic by describing the desired output
Without an MCP server, each of these use cases requires manual context switching: open Weaviate in a browser, find the data you need, copy it, paste it into your AI assistant, and wait for analysis. An MCP server automates that entire data-fetching step.
How to Set Up Weaviate MCP (Community Approach)
Since Weaviate doesn’t have an official MCP server, here’s how to set up a community wrapper or build your own.
weaviate-mcp-server or mcp-weaviate. Look for repositories with recent commits, good documentation, and active maintenance. Check the MCP server registry for officially listed community servers.If a community package exists, install it:
npm install -g @community/weaviate-mcp-server
If building your own, clone a starter template:
git clone https://github.com/modelcontextprotocol/server-template
cd server-template
npm install
Add the server to your Claude Code MCP configuration at ~/.claude.json or your project’s .mcp.json:
{
"mcpServers": {
"weaviate": {
"command": "npx",
"args": [
"@community/weaviate-mcp-server"
],
"env": {
"WEAVIATE_API_KEY": "your-api-key-here"
}
}
}
}
In Cursor, open Settings > MCP Servers and add the same configuration. Cursor uses the same MCP protocol as Claude Code, so most servers work identically in both environments.
{
"mcpServers": {
"weaviate": {
"command": "npx",
"args": ["@community/weaviate-mcp-server"],
"env": {
"WEAVIATE_API_KEY": "your-api-key-here"
}
}
}
}
Restart your editor and test the connection by asking your AI assistant to query Weaviate:
"Can you check if the Weaviate MCP server is connected? Try listing available tools."
If the connection fails, check that your API key is valid, the MCP server process is running, and your firewall isn’t blocking local connections.
Weaviate MCP Server: What You Can and Cannot Do
What Works with a Community Wrapper
Community-built MCP servers for Weaviate typically support read operations – pulling data from the Weaviate API and making it available to your AI assistant. This covers most analytical and reporting use cases.
| Operation | Typically Supported | Notes |
|---|---|---|
| Read data | Yes | Most wrappers expose read endpoints |
| Search/query | Yes | If the Weaviate API supports it |
| Create/write | Sometimes | Depends on wrapper completeness |
| Delete/modify | Rarely | Most wrappers are read-only for safety |
| Webhooks/streaming | Rarely | Requires more complex server architecture |
| Authentication | Usually API key | OAuth flows are harder to implement in MCP |
Known Limitations
- Rate limits: Community wrappers inherit the rate limits of your Weaviate API plan. Aggressive querying through an AI assistant can burn through API quota quickly.
- Data freshness: MCP servers query on demand, so data is as fresh as the API allows. There is no built-in caching in most community wrappers.
- Maintenance risk: Community projects may lag behind Weaviate API changes. Check the repository’s last commit date before relying on it.
- Security: Storing API keys in MCP config files means they sit on disk in plain text. Use environment variables or a secrets manager for production setups.
Weaviate MCP Pricing Considerations
The cost of using Weaviate through MCP depends on several factors:
| Cost Component | Details |
|---|---|
| Weaviate subscription | Your existing Weaviate plan determines API access |
| API usage | Some plans have per-request or rate-limited API access |
| MCP server hosting | Free if running locally; cost if self-hosting remotely |
| Community wrapper | Usually free and open-source |
Important: Some Weaviate plans restrict API access to higher tiers. Verify that your current plan includes the API endpoints the MCP wrapper needs before investing time in setup.
Best MCP Alternatives to Weaviate
If Weaviate doesn’t have a viable MCP server, or if the setup cost isn’t worth it for your use case, these alternatives provide similar capabilities through production-ready MCP servers.
Delulu9 – Best for Keyword Research MCP
If your primary need is keyword research, Delulu9 is purpose-built for MCP from day one. It isn’t a wrapper around another API – it is a native MCP server with its own data infrastructure.
| Feature | Delulu9 | Weaviate (via wrapper) |
|---|---|---|
| Official MCP server | Yes (hosted, maintained) | Community-maintained |
| Setup time | Under 2 minutes | 15-60 minutes |
| Google keyword data | Yes | Depends on wrapper |
| Bing keyword data | Yes | Unlikely |
| Reddit keyword data | Yes | No |
| Search intent categorization | Built-in (automatic) | Manual or unavailable |
| Keyword difficulty scoring | Built-in | Depends on Weaviate API |
| Rate limiting | 1,000 requests/hour | Depends on Weaviate plan |
| Uptime & reliability | 99.9% (hosted infrastructure) | Depends on your setup |
| Price | $12/mo flat | Weaviate subscription + API costs |
| Support | Email + documentation | Community only |
Why Delulu9 Is Different
Most MCP keyword research servers are thin wrappers around third-party APIs. Delulu9 is a vertically integrated tool: it collects data from Google, Bing, and Reddit, processes it through its own intent classification and difficulty scoring pipeline, and serves it through a hosted MCP server.
This matters because:
- No API key chain: You don’t need a separate subscription to another service. One API key, one bill.
- Reddit data is exclusive: No other keyword research MCP server combines Google, Bing, and Reddit autocomplete data in a single query.
- Intent is automatic: Every keyword result includes intent classification (informational, commercial, transactional, navigational) without manual tagging.
- It actually works: The server is hosted, monitored, and maintained. You don’t debug connection issues at 2 AM.
Quick Setup: Delulu9 MCP
{
"mcpServers": {
"delulu9": {
"command": "npx",
"args": ["-y", "@delulu9/seo-mcp-server@latest"],
"env": {
"DELULU9_API_KEY": "your-api-key"
}
}
}
}
That’s it. Restart your editor and ask Claude: “Find keyword ideas for attic conversion using Delulu9.”
How to Choose Between Weaviate MCP and Alternatives
- If you already pay for Weaviate and need its specific data in your AI workflow, invest the time to set up a community MCP wrapper
- If keyword research is your primary need, Delulu9 at $12/mo is faster to set up and more reliable than wrapping another tool’s API
- If you need Weaviate for non-keyword tasks (database queries, data pipeline management, and analytics infrastructure), check whether a community MCP wrapper exists and is actively maintained
- Always verify API access is included in your Weaviate plan before attempting MCP setup
- Consider running community wrappers locally first to test reliability before depending on them
Building Your Own Weaviate MCP Server
If no community wrapper meets your needs, you can build a custom MCP server for Weaviate. Here’s the architecture:
Your Editor (Claude Code / Cursor / Windsurf)
|
v
MCP Protocol (stdio or SSE)
|
v
Your Custom MCP Server (Node.js or Python)
|
v
Weaviate REST API
The MCP SDK is available in TypeScript and Python:
# TypeScript
npm install @modelcontextprotocol/sdk
# Python
pip install mcp
Key decisions when building:
- Which Weaviate API endpoints to expose – start with the 2-3 you use most
- Caching strategy – avoid hitting Weaviate’s API on every query
- Error handling – gracefully handle rate limits and API outages
- Authentication – use environment variables, never hardcode keys
Building a custom server takes 2-8 hours depending on Weaviate’s API complexity. For keyword research, this rarely makes sense when Delulu9 exists at $12/mo.
Weaviate MCP vs REST API: When Do You Need MCP?
Not every Weaviate integration needs MCP. Here’s when each approach makes sense:
| Scenario | Use MCP | Use REST API directly |
|---|---|---|
| AI assistant needs Weaviate data | Yes | Possible but awkward |
| Automated scripts/pipelines | MCP or REST | REST is simpler |
| Browser-based dashboards | No | Yes |
| One-off data pulls | Either works | REST is fine |
| Conversational data exploration | Yes | No |
| Multi-tool AI workflows | Yes | Too complex with REST |
MCP’s value is in the conversational interface. If you’re building traditional automation, the Weaviate REST API works fine. If you want AI assistants to intelligently query and combine data from multiple sources, MCP is the right abstraction.
~/.claude.json. See the step-by-step setup guide above for detailed instructions.
Related Guides
Get Started with MCP Keyword Research
If keyword research is part of your workflow, skip the wrapper complexity. Delulu9 gives you a production-ready MCP server with Google, Bing, and Reddit data, intent classification, and difficulty scoring. $12/mo, 2-minute setup, works with Claude Code, Cursor, and Windsurf.
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Keyword research from Claude Code. Google + Bing + Reddit data. $12/mo after trial.
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