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Airtable OAuth MCP Server

by onimsha

create_record

Add a new entry to an Airtable base by specifying the base, table, and field values for the record.

Instructions

Create a single record

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
base_idYesThe Airtable base ID
table_idYesThe table ID or name
fieldsYesField values for the new record
typecastNoEnable automatic data conversion

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main MCP tool handler function for 'create_record'. It authenticates via OAuth, calls the AirtableClient.create_records method with a single record, and returns the created record details.
    @self.mcp.tool(description="Create a single record")
    async def create_record(
        base_id: Annotated[str, Field(description="The Airtable base ID")],
        table_id: Annotated[str, Field(description="The table ID or name")],
        fields: Annotated[
            dict[str, Any], Field(description="Field values for the new record")
        ],
        typecast: Annotated[
            bool, Field(description="Enable automatic data conversion")
        ] = False,
    ) -> dict[str, Any]:
        """Create a single record in a table."""
        client = await self._get_authenticated_client()
    
        records = await client.create_records(
            base_id,
            table_id,
            [{"fields": fields}],
            typecast,
        )
    
        record = records[0]
        return {
            "id": record.id,
            "fields": record.fields,
            "createdTime": record.created_time,
        }
  • Pydantic schema defining the input arguments for the create_record tool, matching the handler's Annotated fields.
    class CreateRecordArgs(BaseArgs):
        """Arguments for create_record tool."""
    
        base_id: str = Field(description="The Airtable base ID")
        table_id: str = Field(description="The table ID or name")
        fields: dict[str, Any] = Field(description="Field values for the new record")
        typecast: bool | None = Field(
            default=False, description="Enable automatic data conversion"
        )
  • The AirtableClient helper method that makes the actual POST request to Airtable's API to create records. Used by the create_record handler.
    async def create_records(
        self,
        base_id: str,
        table_id: str,
        records: list[dict[str, Any]],
        typecast: bool = False,
    ) -> list[AirtableRecord]:
        """Create new records in a table.
    
        Args:
            base_id: The Airtable base ID
            table_id: The table ID or name
            records: List of record data (each should have 'fields' key)
            typecast: Whether to enable automatic data conversion
    
        Returns:
            List of created records
        """
        logger.info(f"Creating {len(records)} records in {base_id}/{table_id}")
    
        request_data = CreateRecordsRequest(
            records=records,
            typecast=typecast,
        )
    
        response = await self._make_request(
            "POST",
            f"/v0/{base_id}/{table_id}",
            data=request_data.model_dump(by_alias=True, exclude_none=True),
            response_model=CreateRecordsResponse,
        )
    
        return response.records
  • The _register_tools method where all MCP tools, including create_record, are defined and registered using @self.mcp.tool decorators.
    def _register_tools(self) -> None:
Behavior2/5

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

With no annotations, the description carries full burden but only states the action ('create') without disclosing behavioral traits like permissions needed, whether it's idempotent, error handling, or response format. It mentions 'single record' which hints at scope but lacks depth.

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

Conciseness5/5

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

The description is extremely concise ('Create a single record') with no wasted words, front-loaded and to the point. It's efficient, though this brevity contributes to gaps in other dimensions.

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 a mutation tool with no annotations but a rich input schema (4 params, 100% coverage) and an output schema (implied by context signals), the description is minimal. It covers the basic action but lacks details on behavior, usage, or output, making it adequate but incomplete for full agent understanding.

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 100%, so the schema fully documents parameters like 'base_id', 'table_id', 'fields', and 'typecast'. The description adds no extra meaning beyond implying a single record creation, aligning with the baseline for high coverage.

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 'Create a single record' clearly states the verb ('create') and resource ('record'), but it's vague about the context (Airtable) and doesn't distinguish from siblings like 'create_records' (plural vs. single). It's functional but lacks specificity 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 'create_records' or 'update_records', nor does it mention prerequisites or context. It's a bare statement with no usage instructions.

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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