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batch_create_bitable_records

Add multiple records to Feishu Bitable tables in bulk operations, supporting up to 1000 entries per request for efficient data management.

Instructions

    批量创建多维表格记录(最多1000条)

    参数:
        app_token: 多维表格的token
        table_id: 数据表ID
        records: 记录列表,每条记录是一个fields字典

    返回:
        创建的记录信息
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
app_tokenYes
table_idYes
recordsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The core handler function for the 'batch_create_bitable_records' tool. It uses the Lark_oapi client to batch create records in a Bitable app table, with input parameters app_token, table_id, and records list.
    @mcp.tool()
    @handle_feishu_error
    def batch_create_bitable_records(
        app_token: str, table_id: str, records: list[dict]
    ) -> str:
        """
        批量创建多维表格记录(最多1000条)
    
        参数:
            app_token: 多维表格的token
            table_id: 数据表ID
            records: 记录列表,每条记录是一个fields字典
    
        返回:
            创建的记录信息
        """
        client = get_client()
        request = (
            BatchCreateAppTableRecordRequest.builder()
            .app_token(app_token)
            .table_id(table_id)
            .request_body(
                lark.BatchCreateAppTableRecordRequestBody.builder()
                .children(records)
                .build()
            )
            .build()
        )
        response = client.bitable.v1.app_table_record.batch_create(request)
        return lark.JSON.marshal(response.data, indent=4)
  • Top-level registration of bitable tools, including the call to register_bitable_record_tools(mcp) which defines and registers the batch_create_bitable_records tool.
    # 注册多维表格工具
    register_bitable_app_tools(mcp)
    register_bitable_record_tools(mcp)
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the 1000-record limit (useful context) but doesn't describe authentication needs, rate limits, error handling, or what happens when the limit is exceeded. For a batch creation tool with zero annotation coverage, this leaves significant behavioral gaps.

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

Conciseness4/5

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

The description is appropriately sized with clear sections for purpose, parameters, and return value. Each sentence adds value, though the parameter explanations could be more detailed given the 0% schema coverage. The structure is logical and front-loaded with the core purpose.

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

Completeness3/5

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

Given 3 parameters with 0% schema coverage and no annotations, the description provides basic parameter semantics but lacks behavioral context. The presence of an output schema reduces the need to explain return values, but for a batch creation tool, more guidance on usage, constraints, and error scenarios would be beneficial.

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

Parameters3/5

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. It lists all three parameters with brief explanations, adding meaning beyond the bare schema. However, it doesn't provide format details (e.g., what fields dictionary contains, token format) or constraints beyond the 1000-record limit mentioned in the purpose section.

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

Purpose4/5

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

The description clearly states the verb ('批量创建' - batch create) and resource ('多维表格记录' - multi-dimensional table records) with a specific scope ('最多1000条' - up to 1000 records). It distinguishes from the sibling 'create_bitable_record' by indicating batch capability, though it doesn't explicitly mention this distinction.

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?

No guidance is provided on when to use this tool versus alternatives like 'create_bitable_record' (single record creation) or other batch operations. The description mentions the 1000-record limit but doesn't explain when batch creation is preferred over individual creation or how it relates to other batch operations in the sibling list.

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

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