MCP (Model Context Protocol) server for Weaviate
| Publisher | weaviate |
| Repository | mcp-server-weaviate |
| Language | Go |
| Forks | 43 |
| Stars | 161 |
| Available tools | 15 |
| Transport type | stdio |
| Categories | |
| License | MIT |
| Links |
Weaviate exposes MCP capabilities that can be used by compatible AI clients and agents.
Browse the callable actions below, including names and descriptions when provided by the server.
Use the installation snippets to configure this server in your preferred MCP client.
161 stars and 43 forks from the linked repository.
Build the server:
make build
Run the test client
make run-client
Insert an object into weaviate.
Request body:
json{}
Response body
json{}
Retrieve objects from weaviate with hybrid search.
Request body:
json{}
Response body
json{}
Node.js 18+
{
"mcpServers": {
"weaviate": {
"command": "uvx",
"args": [
"mcp-server-weaviate"
],
"env": {
"WEAVIATE_URL": "<YOUR_WEAVIATE_URL>",
"WEAVIATE_API_KEY": "<YOUR_API_KEY>"
}
}
}
}Create a new collection (class) in Weaviate with specified schema and vectorizer configuration
List all collections (classes) available in the Weaviate instance with their schemas
Retrieve the detailed schema configuration for a specific collection
Delete an entire collection and all its objects from Weaviate
Add a new object with properties and vector embedding to a specified collection
Add multiple objects to a collection in a single batch operation for better performance
Update properties of an existing object by its UUID
Delete a specific object from a collection by its UUID
Retrieve a specific object by its UUID with all properties and metadata
Perform semantic similarity search using vector embeddings to find related objects
Perform keyword-based search using BM25 algorithm across object properties
Combine semantic and keyword search for more comprehensive results
Perform search with generative AI to create answers based on search results
Query objects using Where filters based on property values and conditions
Check the health and status of the Weaviate cluster and nodes
TypingMind connects MCP tools at the workspace level, so once Weaviate is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.
Use this when the MCP server needs access to local files, apps, or private resources on your computer.
In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.
Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.
When the connector status is Ready, click Edit Servers and paste the MCP server configuration.
{
"mcpServers": {
"weaviate": {
"command": "npx",
"args": [
"-y",
"mcp-server-weaviate"
]
}
}
}Save the server list, open Plugins, enable the Weaviate MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.
Weaviate is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.
Yes. TypingMind connects MCP tools at the workspace level, so you can use Weaviate with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.
TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Weaviate connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.
Weaviate runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.
Weaviate exposes 15 MCP tools that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.
No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Weaviate requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.
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