databases

Lark Base

lark-base-team

by lark-base-team

Connect to Lark Base for seamless spreadsheet and database management, including data retrieval and field edits with sec

Integrates with Lark Base (formerly Feishu Bitable) to enable data retrieval, field management, and table manipulation across collaborative spreadsheets and databases using app tokens and personal base tokens for authentication.

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Full read-write access to Lark BaseRequires app tokens and personal base tokens

best for

  • / Teams using Lark/Feishu for collaborative data management
  • / Automating data entry and updates in corporate spreadsheets
  • / Building integrations with Lark Base databases

capabilities

  • / List and browse tables and records
  • / Create and delete tables in base apps
  • / Manage fields (create, update, delete)
  • / Insert new records into tables
  • / Update existing table structures
  • / Retrieve field information and schemas

what it does

Connects to Lark Base (formerly Feishu Bitable) collaborative spreadsheets and databases to read, write, and manage data programmatically. Provides full CRUD operations on tables, fields, and records.

about

Lark Base is an official MCP server published by lark-base-team that provides AI assistants with tools and capabilities via the Model Context Protocol. Connect to Lark Base for seamless spreadsheet and database management, including data retrieval and field edits with sec It is categorized under databases. This server exposes 13 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install Lark Base in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Lark Base is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

base-mcp-server

A Model Context Protocol server that provides read and write access to Feishu Base (飞书多维表格) databases. This server enables LLMs to inspect database schemas, then read and write records.

Usage

1. Install Node.js

Make sure Node.js is installed on your machine.

2. Obtain Base Tokens

Get the appToken and personalBaseToken for your base account.

3. Install MCP Server Package Globally

Install the MCP server package globally using npm:

npm install -g @lark-base-open/mcp-node-server

4. MCP Server Configuration

In your MCP server configuration file, add the following:

{
  "mcpServers": {
    "base-mcp-server": {
      "command": "npx",
      "args": [
        "@lark-base-open/mcp-node-server",
        "-a",
        "appToken of base",
        "-p",
        "personalBaseToken of base"
      ]
    }
  }
}

Note: If you are using Claude, you will need to add the MCP configuration through the Developer option in the Claude client settings. You can access this in the Settings menu, and then add the MCP server details under the relevant section.

tokens

You need get two tokens before using this mcp server.

  • personalBaseToken: find Base Plugin UI in your base, and access Custom Plugin->Get Authorization Code Video

  • appToken: You can obtain the appToken quickly through a Developement Tool plugin. Here’s a simplified step-by-step process on how to do it: Video

Components

Tools

  • list_tables

    • Lists all tables in a base
    • No input parameters required
  • list_records

    • Lists records from a specified table
    • Input parameters:
      • tableId (string, required): The ID of the table to query
  • get_record

    • Gets a specific record by ID
    • Input parameters:
      • tableId (string, required): The ID of the table
      • recordId (string, required): The ID of the record to retrieve
  • create_record

    • Creates a new record in a table
    • Input parameters:
      • tableId (string, required): The ID of the table
      • fields (object, required): The fields and values for the new record
  • update_record

    • Updates a record in a table
    • Input parameters:
      • tableId (string, required): The ID of the table
      • recordId (string, required): The ID of the record
      • fields (object, required): The fields to update and their new values
  • delete_record

    • Deletes a record from a table
    • Input parameters:
      • tableId (string, required): The ID of the table
      • recordId (string, required): The ID of the record to delete
  • create_table

    • Creates a new table in a base
    • Input parameters:
      • name (string, required): Name of the new table
      • fields (array, required): Array of field definitions (name, type, description, options)
  • update_table

    • Updates a table's name
    • Input parameters:
      • tableId (string, required): The ID of the table
      • name (string, required): New name for the table
  • delete_table

    • Deletes a table
    • Input parameters:
      • tableId (string, required): The ID of the table to delete
  • list_fields

    • Lists all fields in a table
    • Input parameters:
      • tableId (string, required): The ID of the table
  • create_field

    • Creates a new field in a table
    • Input parameters:
      • tableId (string, required): The ID of the table
      • nested (object, required): Field configuration object containing:
        • field (object, required): Field definition with name, type, and other properties
  • update_field

    • Updates a field in a table
    • Input parameters:
      • tableId (string, required): The ID of the table
      • fieldId (string, required): The ID of the field
      • nested (object, required): Updated field configuration
  • delete_field

    • Deletes a field from a table
    • Input parameters:
      • tableId (string, required): The ID of the table
      • fieldId (string, required): The ID of the field to delete

Development

To get started with development:

  1. Install Node.js
  2. Clone the repository
  3. Install dependencies with npm install
  4. Run npm dev to start the development server
  5. Run npm test to run tests
  6. Build with npm build

Available scripts:

  • npm dev: Build and run the server in development mode
  • npm start: Run the server
  • npm test: Run tests
  • npm test:watch: Run tests in watch mode
  • npm lint: Run ESLint
  • npm build: Build the project
  • npm build:watch: Watch for changes and rebuild automatically

Project Structure

.
├── src/                # Source code
│   ├── index.ts       # Main entry point(stdio)
│   ├── index.sse.ts   # SSE entry point
│   ├── service/       # Service implementations
│   ├── types/         # TypeScript type definitions
│   ├── utils/         # Utility functions
│   └── test/          # Test files
├── dist/              # Compiled output

Dependencies

Main dependencies:

  • @lark-base-open/node-sdk: Feishu Base Node.js SDK
  • @modelcontextprotocol/sdk: Model Context Protocol SDK
  • express: Web framework
  • zod: Schema validation

License

MIT

FAQ

What is the Lark Base MCP server?
Lark Base is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Lark Base?
This profile displays 53 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Direct Database Queries from AI

Enable Claude to query your database directly using natural language

Example

Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly

Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster

Data Analysis & Reporting

Generate complex reports and analytics without leaving conversation

Example

Analyze sales trends, cohort retention, user behavior patterns conversationally

Democratize data access—non-technical team members can query databases

Schema Exploration

Understand database structure, relationships, and data models

Example

'Explain the user_orders table schema and its relationships'

Onboard engineers faster, explore unfamiliar databases efficiently

Data Validation & Quality Checks

Run data quality queries to catch anomalies and inconsistencies

Example

Find duplicate records, missing values, orphaned foreign keys automatically

Maintain data integrity with less manual SQL work

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor with MCP support
  • Database credentials (read-only recommended for safety)
  • Network access from Claude client to database
  • Understanding of database security and access control

Time Estimate

15-30 minutes including configuration and testing

Installation Steps

  1. 1.Install MCP server: npm install -g @modelcontextprotocol/server-[name]
  2. 2.Configure database connection in Claude Desktop config (~/.claude/mcp.json)
  3. 3.Provide connection string: host, port, database, username, password
  4. 4.Restart Claude Desktop to load MCP server
  5. 5.Test connection: 'List all tables in database'
  6. 6.Run simple query: 'Show me 5 rows from users table'
  7. 7.Verify results and permissions are correct
  8. 8.Document query patterns for team use

Troubleshooting

  • Connection refused: Check database is running and network accessible
  • Authentication failed: Verify credentials, check user permissions
  • Claude can't see tables: Grant appropriate read permissions to database user
  • Slow queries: Add indexes, limit result set size, use read replicas
  • MCP server not loading: Check config syntax, restart Claude Desktop

Best Practices

✓ Do

  • +Use read-only database credentials to prevent accidental writes
  • +Connect to read replica, not production primary database
  • +Set query timeout limits to prevent long-running queries
  • +Document database schema and common queries for AI context
  • +Monitor query performance and optimize slow queries
  • +Use connection pooling for better performance
  • +Test with non-production data first

✗ Don't

  • Don't use production write credentials—risk of data corruption
  • Don't query production database during peak traffic hours
  • Don't expose sensitive PII without proper access controls
  • Don't skip query result validation—AI can misinterpret schema
  • Don't allow unlimited result set sizes—set LIMIT clauses
  • Don't share database credentials in plain text config files

💡 Pro Tips

  • Create database views for common queries to simplify AI access
  • Add schema comments/descriptions so AI understands column meanings
  • Use semantic table/column names ('customer_lifetime_value' not 'clv')
  • Set up query logging to audit what Claude is querying
  • Create saved query templates for recurring analysis
  • Combine with data visualization tools for better insights

Technical Details

Architecture

MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.

Protocols

  • Model Context Protocol (MCP)
  • Database-specific protocols (PostgreSQL, MySQL, MongoDB)

Compatibility

  • PostgreSQL
  • MySQL
  • SQLite
  • MongoDB
  • Redis

When to Use This

✓ Use When

Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.

✗ Avoid When

Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.

Integration

  • Read replica connection for analytics queries
  • Database view layer to abstract complex joins
  • Query result caching for repeated questions
  • Audit logging of all AI-generated queries

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.653 reviews
  • Sophia Thomas· Dec 28, 2024

    Useful MCP listing: Lark Base is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Dhruvi Jain· Dec 24, 2024

    I recommend Lark Base for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Naina Smith· Dec 12, 2024

    I recommend Lark Base for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Sophia Wang· Dec 8, 2024

    Lark Base reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Amina Verma· Nov 27, 2024

    Lark Base is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Oshnikdeep· Nov 15, 2024

    Strong directory entry: Lark Base surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Min Mehta· Nov 3, 2024

    Strong directory entry: Lark Base surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Ira Reddy· Oct 22, 2024

    Lark Base is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Alexander Flores· Oct 18, 2024

    Strong directory entry: Lark Base surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Ganesh Mohane· Oct 6, 2024

    Lark Base is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

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