Append rows to an Iceberg table using PyIceberg/Daft. This tool appends data rows to an existing Iceberg table using the PyIceberg engine. The rows parameter must be a list of dictionaries, each representing a row. Check the schema of the table before appending rows. Example input values: w...
Accepts URL/endpoint input (uri)
Part of the AWS S3 Tables MCP Server MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents call append_rows_to_table to retrieve information from AWS S3 Tables MCP Server without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though append_rows_to_table only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
tools:
append_rows_to_table:
rules:
- action: allowSee the full AWS S3 Tables MCP Server policy for all 16 tools.
Agents calling read-class tools like append_rows_to_table have been implicated in these attack patterns. Read the full case and prevention policy for each:
Other tools in the Read risk category across the catalogue. The same policy patterns (rate-limit, allow) apply to each.
Append rows to an Iceberg table using PyIceberg/Daft. This tool appends data rows to an existing Iceberg table using the PyIceberg engine. The rows parameter must be a list of dictionaries, each representing a row. Check the schema of the table before appending rows. Example input values: warehouse: 'arn:aws:s3tables:<Region>:<accountID>:bucket/<bucketname>' region: 'us-west-2' namespace: 'retail_data' table_name: 'customers' rows: [{"customer_id": 1, "customer_name": "Alice"}, ...] uri: 'https://s3tables.us-west-2.amazonaws.com/iceberg' catalog_name: 's3tablescatalog' rest_signing_name: 's3tables' rest_sigv4_enabled: 'true'. It is categorised as a Read tool in the AWS S3 Tables MCP Server MCP Server, which means it retrieves data without modifying state.
Add a rule in your Intercept YAML policy under the tools section for append_rows_to_table. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the AWS S3 Tables MCP Server MCP server.
append_rows_to_table is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the append_rows_to_table rule in your Intercept policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the Intercept policy for append_rows_to_table. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
append_rows_to_table is provided by the AWS S3 Tables MCP Server MCP server (awslabs.s3-tables-mcp-server). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic policy on every MCP tool call. Per-identity grants. Full audit log.