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Bitable MCP Server

by lloydzhou
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Python
Remote

Bitable MCP Server

This MCP server provides access to Lark Bitable through the Model Context Protocol. It allows users to interact with Bitable tables using predefined tools.

One click installation & Configuration

Installing via Smithery

To install Bitable Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @lloydzhou/bitable-mcp --client claude

Claude

To install and configure the server, use the following command:

PERSONAL_BASE_TOKEN=your_personal_base_token APP_TOKEN=your_app_token uv run --with uv --with bitable-mcp bitable-mcp-install

Replace your_personal_base_token and your_app_token with your actual tokens.

Cursor

Coming soon

Windsurf

Coming soon

Related MCP server: Lark Base MCP Server

Available Tools

  • list_table - List tables for the current Bitable.

    • Returns: A JSON-encoded list of table names.

  • describe_table - Describe a table by its name.

    • Parameters:

      • name (str): The name of the table to describe.

    • Returns: A JSON-encoded list of columns in the table.

  • read_query - Execute a SQL query to read data from the tables.

    • Parameters:

      • sql (str): The SQL query to execute.

    • Returns: A JSON-encoded list of query results.

Manual installation and configuration

Please make sure uvx is installed before installation.

Add to your Claude settings:

  1. Using uvx

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

  1. Using pip installation

  1. Install bitable-mcp via pip:

pip install bitable-mcp

  1. Modify your Claude settings

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

Configure for Zed

Add to your Zed 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 installation

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

Debugging

You can use the MCP inspector to debug the server. For uvx installations:

npx @modelcontextprotocol/inspector uvx bitable-mcp

Available Tools

3 tools
describe_tableD

describe_table by table name

ParametersJSON Schema
NameRequiredDescriptionDefault
nameYes

TDQS

D1.5/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden for behavioral disclosure. The description reveals nothing about what the tool actually does behaviorally: whether it's a read operation, what information it returns, if it requires specific permissions, potential side effects, error conditions, or performance characteristics. 'describe_table' implies a read operation, but this isn't explicitly stated or detailed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at just three words, which could be appropriate if it were more informative. However, this brevity results in under-specification rather than efficient communication. While it's front-loaded with the core action, the single phrase doesn't earn its place by providing sufficient value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a tool with 1 parameter (0% schema coverage), no annotations, no output schema, and sibling tools that suggest database/query operations, the description is completely inadequate. It doesn't explain what the tool returns, how it differs from siblings, what 'describe' means in this context, or any behavioral characteristics. For a tool that presumably returns table metadata or schema information, this leaves critical gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. The description mentions 'by table name' which hints at the 'name' parameter, but adds no semantic meaning beyond what's obvious from the parameter name itself. It doesn't explain what constitutes a valid table name, format expectations, or how the parameter influences the tool's behavior.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'describe_table by table name' is essentially a tautology that restates the tool name with minimal additional information. It mentions the action ('describe_table') and the parameter ('by table name'), but doesn't specify what describing entails (e.g., returning schema, metadata, statistics) or what resource is being described. It doesn't distinguish this tool from its sibling 'list_table' or 'read_query' beyond the obvious name difference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to use describe_table instead of list_table (which might list tables without details) or read_query (which might execute queries). There are no prerequisites, exclusions, or contextual cues about appropriate usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_tableC

list table for current bitable

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

C2.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the action ('list table') without details on permissions, rate limits, pagination, or what 'current bitable' entails. This is inadequate for a tool with zero annotation coverage, as it lacks critical behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single phrase ('list table for current bitable'), which is concise but potentially under-specified. It lacks structure or front-loading of key details, making it efficient but not optimally informative for an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no output schema), the description is incomplete. It doesn't explain what 'current bitable' means, how results are returned, or differentiate from siblings, leaving gaps in context despite low complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and the schema description coverage is 100% (though empty). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters, as there's nothing to compensate for.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool's purpose as 'list table for current bitable', which clearly indicates a listing operation on tables. However, it doesn't specify what 'current bitable' means or how this differs from sibling tools like 'describe_table' and 'read_query', making it somewhat vague about scope and differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'describe_table' or 'read_query'. It doesn't mention any prerequisites, context for 'current bitable', or exclusions, leaving the agent without usage instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

read_queryD

read_query by sql

ParametersJSON Schema
NameRequiredDescriptionDefault
sqlYes

TDQS

D1.3/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description reveals nothing about what this tool actually does behaviorally - whether it executes SQL queries, what permissions are required, whether it's read-only or has side effects, what happens with invalid SQL, or what format results are returned in. 'read_query by sql' is completely inadequate for a tool that presumably executes database queries.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While technically concise with just three words, this is under-specification rather than effective conciseness. The description is so sparse that it fails to communicate essential information. Every word should earn its place, but here the words don't provide enough value to justify their inclusion - they create ambiguity rather than clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a SQL query execution tool with no annotations, no output schema, and a single undocumented parameter, the description is completely inadequate. It doesn't explain what the tool returns, what errors might occur, what database context it operates in, or any behavioral characteristics. This leaves the agent with insufficient information to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage and one required parameter (sql), the description provides no meaningful information about the parameter. It doesn't explain what SQL syntax is expected, what database it runs against, whether it supports parameterized queries, or any constraints on the SQL. The description fails completely to compensate for the lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'read_query by sql' is a tautology that essentially restates the tool name with minimal additional information. It mentions 'read' and 'sql' but doesn't specify what resource is being read (tables? databases? specific data?), nor does it distinguish this tool from its siblings (describe_table, list_table). The purpose remains vague rather than specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is absolutely no guidance about when to use this tool versus the sibling tools (describe_table, list_table). The description provides no context about appropriate use cases, prerequisites, or alternatives. This leaves the agent with no information to make an informed selection between available tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updates
    • First observeddescribe_table
    • First observedlist_table
    • First observedread_query
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TDQS

C2.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: describe_table inspects table structure, list_table enumerates available tables, and read_query executes SQL queries. An agent can easily differentiate between metadata operations and data querying.

Naming Consistency4/5

The naming follows a consistent verb_noun pattern (describe_table, list_table, read_query) with minor deviation where 'read_query' uses 'read' instead of a more typical CRUD verb like 'execute'. All names use snake_case consistently.

Tool Count3/5

With only 3 tools, the server feels thin for a database/bitable domain. While core operations are covered, typical expectations might include create/update/delete tools. The count is borderline but functional for basic querying and inspection.

Completeness2/5

The toolset is severely incomplete for a bitable server. There are no tools for creating, updating, or deleting tables or records, which are fundamental CRUD operations. Agents will hit dead ends when trying to modify data, limiting this to read-only use cases.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

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