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Typesense
- python
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python
Language
6 months ago
First Indexed
2 months ago
Catalog Refreshed
Documentation & install
Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.
Installation
Add the following to your MCP client configuration file.
Configuration
View docs{
"mcpServers": {
"avarant-typesense-mcp-server": {
"command": "uv",
"args": [
"--directory",
"~/typesense-mcp-server",
"run",
"mcp",
"run",
"main.py"
]
}
}
}You have an MCP server that bridges Typesense with the Model Context Protocol, enabling you to manage collections, documents, and searches via MCP-compatible clients. This server exposes a set of tools to health-check, describe, index, and query your Typesense data, all through standardized MCP commands you can integrate into your apps and workflows.
How to use
You use this MCP server by calling its available MCP tools through an MCP client. The server exposes capabilities to check health, list and describe collections, create and delete collections, and to index, update, or remove documents. You can perform searches and vector similarity searches against your configured Typesense collections. Think of it as a bridge that lets your MCP client issue high-level data operations against Typesense without talking to the Typesense API directly.
How to install
Prerequisites you need before installing include a runtime environment for the server and the ability to run the MCP runtime you choose.
Manual installation steps you should follow exactly as written below to set up the server locally.
# Prerequisite: install uv on your system (for local runtime)
# Mac users can install via Homebrew
brew install uv
# Clone the MCP server package locally
git clone git@github.com:avarant/typesense-mcp-server.git ~/typesense-mcp-server
# Update your Cursor MCP config to use this server
# Add the following to your .cursor/mcp.json under mcpServers
``````json
{
"mcpServers": {
"typesense": {
"command": "uv",
"args": ["--directory", "~/typesense-mcp-server", "run", "mcp", "run", "main.py"],
"env": {
"TYPESENSE_HOST": "",
"TYPESENSE_PORT": "",
"TYPESENSE_PROTOCOL": "",
"TYPESENSE_API_KEY": ""
}
}
}
}
## Additional setup guidance
If you prefer an automated installation path, you can install the MCP server through your MCP client’s installer workflow (if available for your environment). The manual steps above provide a local development flow that you can adapt for production by wiring your environment variables and persistent storage as needed.
## Available tools
### check\_typesense\_health
Checks the health status of the configured Typesense server
### list\_collections
Retrieves a list of all collections in the Typesense server
### describe\_collection
Retrieves the schema and metadata for a specific collection
### export\_collection
Exports all documents from a specific collection
### create\_collection
Creates a new collection with the provided schema
### delete\_collection
Deletes a specific collection
### truncate\_collection
Truncates a collection by deleting all documents but keeping the schema
### create\_document
Creates a single new document in a specific collection
### upsert\_document
Upserts (creates or updates) a single document in a specific collection
### index\_multiple\_documents
Indexes (creates, upserts, or updates) multiple documents in a batch
### delete\_document
Deletes a single document by its ID from a specific collection
### import\_documents\_from\_csv
Imports documents from CSV data into a collection
### search
Performs a keyword search on a specific collection
### vector\_search
Performs a vector similarity search on a specific collection