About Typesense MCP Server
Typesense MCP Server is an MCP server that gives AI models access to Typesense search. It lets LLMs discover, search and analyze data stored in Typesense collections.
Key features
- 01Collections exposed as typesense:// resources
- 02Tools for listing collections, searching and fetching documents
- 03Tool for collection statistics and schema information
- 04Prompts for collection analysis and search suggestions
Use cases
- 01Letting an LLM discover collections at runtime and choose which to search
- 02Running filtered and sorted searches over Typesense collections
- 03Reviewing a collection's schema and document count to understand its data
MCP support
This project is an MCP server that exposes Typesense collection resources, search and statistics tools, and the analyze_collection and search_suggestions prompts to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMETypesense MCP Server
A Model Context Protocol (MCP) server implementation that provides AI models with access to Typesense search capabilities. This server enables LLMs to discover, search, and analyze data stored in Typesense collections.
Demo
Features
Resources
- List and access collections via
typesense://URIs - Each collection has a name, description, and document count
- JSON mime type for schema access
Tools
-
typesense_query
- Search for documents in Typesense collections with powerful filtering
- Input: Query text, collection name, search fields, filters, sort options, limit
- Returns matching documents with relevance scores
-
typesense_get_document
- Retrieve specific documents by ID from collections
- Input: Collection name, document ID
- Returns complete document data
-
typesense_collection_stats
- Get statistics about a Typesense collection
- Input: Collection name
- Returns collection metadata, document count, and schema information
Prompts
-
analyze_collection
- Analyze collection structure and contents
- Input: Collection name
- Output: Insights about schema, data types, and statistics
-
search_suggestions
- Get suggestions for effective search queries for a collection
- Input: Collection name
- Output: Recommended search strategies based on collection schema
Installation
Via npm
# Global installation
npm install -g typesense-mcp-server
# Local installation
npm install typesense-mcp-server
Via mcp-get
npx @michaellatman/mcp-get@latest install typesense-mcp-server
Development
Install dependencies:
npm install
Build the server:
npm run build
For development with auto-rebuild:
npm run watch
Installation for Development
Using Claude Desktop
To use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"typesense": {
"command": "node",
"args": [
"~/typesense-mcp-server/dist/index.js",
"--host", "your-typesense-host",
"--port", "8108",
"--protocol", "http",
"--api-key", "your-api-key"
]
},
}
}
Debugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:
npm run inspector
The Inspector will provide a URL to access debugging tools in your browser.
Components
Resources
The server provides information about Typesense collections:
- Collection Schemas (
typesense://collections/<collection>)- JSON schema information for each collection
- Includes field names and data types
- Sample documents for understanding data structure
Tools
The server provides the following tools for interacting with Typesense:
-
typesense_list_collections - List all available collections with their schemas
- Enables zero-conf discovery and routing
- LLM can enumerate collections at runtime and pick the right one(s) before searching
- Useful when collections vary by environment, tenant, or version
- Returns field definitions for schema inference (searchable, facetable, numeric fields)
- Supports
include_fieldsparameter to control detail level
-
typesense_query - Search for documents in a collection
- Full-text search with customizable parameters
- Supports filtering, sorting, and pagination
-
typesense_get_document - Retrieve a specific document by ID
-
typesense_collection_stats - Get detailed statistics about a specific collection
Resource Templates
The server provides templates for:
- typesense_search - Template for constructing Typesense search queries
- typesense_collection - Template for viewing Typesense collection details
Usage with Claude Desktop
To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your claude_desktop_config.json:
{
"mcpServers": {
"typesense": {
"command": "npx",
"args": [
"-y",
"typesense-mcp-server",
"--host", "your-typesense-host",
"--port", "8108",
"--protocol", "http",
"--api-key", "your-api-key"
]
}
}
}
Logging
The server logs information to a file located at:
/tmp/typesense-mcp.log
This log contains detailed information about server operations, requests, and any errors that occur.
License
This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.

