AppFlowy Cloud MCP Server
Enables interaction with the AppFlowy Cloud API to manage workspaces, databases, and row operations. It provides tools for authentication, resource discovery, and full row manipulation including creation and upserts.
README
AppFlowy Cloud MCP Server
A Model Context Protocol (MCP) server for interacting with AppFlowy Cloud API, providing tools for workspace, database, and row operations.
Features
- Authentication: Login and refresh token management
- Workspace Operations: List all workspaces
- Database Operations: List databases, get database fields
- Row Operations: List rows, get row details, create rows, upsert rows
Authentication
The server uses in-memory token storage. To authenticate:
- Use
appflowy_loginwith your email and password - The tokens are stored automatically
- Use
appflowy_refresh_tokenwhen the access token expires
Available Tools
Authentication Tools
appflowy_login(request: LoginRequest)- Login to AppFlowy Cloudappflowy_refresh_token(request: RefreshTokenRequest)- Refresh access token
Workspace Tools
appflowy_list_workspaces()- List all workspaces
Database Tools
appflowy_list_databases(workspace_id: str)- List databases in a workspaceappflowy_get_database_fields(workspace_id: str, database_id: str)- Get database fields
Row Tools
appflowy_list_rows(workspace_id: str, database_id: str)- List row IDsappflowy_get_row_details(workspace_id: str, database_id: str, row_ids: str, with_doc: bool = False)- Get row detailsappflowy_create_row(workspace_id: str, database_id: str, request: RowCreateRequest)- Create a new rowappflowy_upsert_row(workspace_id: str, database_id: str, request: RowUpdateRequest)- Update or create row
Running the Server
uv run python main.py
Usage Example
- Login:
request = LoginRequest(email="your@example.com", password="your_password")
response = appflowy_login(request)
- List workspaces:
workspaces = appflowy_list_workspaces()
- Get database fields:
fields = appflowy_get_database_fields("workspace_id", "database_id")
- Create a row:
row_request = RowCreateRequest(cells={"Field_Name": "Value"}, document="Optional markdown")
result = appflowy_create_row("workspace_id", "database_id", row_request)
Note
The server maintains tokens in memory. For production use, consider adding persistent storage (Redis, database, etc.) and proper error handling.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.