Bitable MCP Server Integration

What does “Bitable” MCP Server do?

The Bitable MCP Server provides seamless access to Lark Bitable, a collaborative spreadsheet and database platform, through the Model Context Protocol (MCP). This server enables AI assistants and developer tools to interact directly with Bitable tables using predefined tools. With Bitable MCP, users can automate database operations such as listing available tables, describing table schemas, and querying data using SQL-like statements. This MCP server streamlines workflows involving data extraction, management, and integration, making it easier to build intelligent assistants or automation pipelines that interact with structured data in Lark Bitable. Its integration with MCP also ensures compatibility with various AI platforms and development environments, enhancing productivity for developers and users working with data-driven applications.

List of Prompts

No prompt templates are mentioned in the repository or documentation.

List of Resources

No explicit MCP resources are listed in the available documentation or code.

List of Tools

• list_table
  List tables for the current Bitable instance. Returns a JSON-encoded list of table names.
• describe_table
  Describe a table by its name. Takes a name parameter (string) and returns a JSON-encoded list of columns in the table.
• read_query
  Execute a SQL query to read data from the tables. Takes a sql parameter (string) and returns a JSON-encoded list of query results.

Use Cases of this MCP Server

• Database Table Discovery
  Developers and AI agents can quickly list all tables in a Bitable workspace, making it easier to navigate and select relevant data sources.
• Schema Exploration
  By describing table schemas, users can understand the structure of tables, including columns and data types, which aids in building robust queries or data integrations.
• Automated Data Extraction
  With SQL-like querying, users can extract specific slices of data for reporting, dashboarding, or feeding into downstream applications.
• AI-Assisted Data Analysis
  AI assistants can leverage these tools to automate analysis, answer data questions, or summarize insights from Bitable tables.
• Workflow Automation
  Integrate with other tools or platforms (like Claude or Zed) to trigger data-driven workflows such as syncing, cleaning, or aggregating records.

How to set it up

Windsurf

No setup instructions provided for Windsurf. Marked as “Coming soon” in the documentation.

Claude

1. Ensure you have uvx installed.

2. Obtain your PERSONAL_BASE_TOKEN and APP_TOKEN from Lark Bitable.

3. Add the following to your Claude settings:

   "mcpServers": {
     "bitable-mcp": {
       "command": "uvx",
       "args": ["bitable-mcp"],
       "env": {
           "PERSONAL_BASE_TOKEN": "your-personal-base-token",
           "APP_TOKEN": "your-app-token"
       }
     }
   }
   

4. Alternatively, install via pip and update settings:

   pip install bitable-mcp
   

   "mcpServers": {
     "bitable-mcp": {
       "command": "python",
       "args": ["-m", "bitable_mcp"],
       "env": {
           "PERSONAL_BASE_TOKEN": "your-personal-base-token",
           "APP_TOKEN": "your-app-token"
       }
     }
   }
   

5. Save your configuration and restart Claude.

Securing API Keys:
Store sensitive keys using env in your JSON config:

"env": {
  "PERSONAL_BASE_TOKEN": "your-personal-base-token",
  "APP_TOKEN": "your-app-token"
}

Cursor

No setup instructions provided for Cursor. Marked as “Coming soon” in the documentation.

Cline

No setup instructions provided for Cline.

Zed

For Zed, add to your settings.json:

Using uvx:

"context_servers": [
  "bitable-mcp": {
    "command": "uvx",
    "args": ["bitable-mcp"],
    "env": {
        "PERSONAL_BASE_TOKEN": "your-personal-base-token",
        "APP_TOKEN": "your-app-token"
    }
  }
],

Using pip:

"context_servers": {
  "bitable-mcp": {
    "command": "python",
    "args": ["-m", "bitable_mcp"],
    "env": {
        "PERSONAL_BASE_TOKEN": "your-personal-base-token",
        "APP_TOKEN": "your-app-token"
    }
  }
},

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "bitable-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change "bitable-mcp" to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

• Roots support: Not mentioned
• Sampling support: Not mentioned

Our opinion

The Bitable MCP server is straightforward and focused, offering essential tools for database interaction (listing, schema, query). There is no evidence of prompt templates or explicit MCP resources, and setup is only fully documented for Claude and Zed. The repository is open but basic, with no clear sign of advanced MCP features like roots or sampling.

MCP Table rating: 5/10.
It covers the basics well and is usable, but lacks documentation depth, resources, prompts, and advanced MCP features.

MCP Score