查询表中的数据
AI agents call select_data to retrieve information from PySqlitMCP without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The tool name and description both indicate querying/selecting data from a table, which is a read operation with no side effects. However, the server also exposes 'execute_sql_statement' which could run arbitrary SQL, but this specific tool appears limited to SELECT queries.
From the tool's definition Tool name 'select_data' and description '查询表中的数据' (query data from a table) indicate a read-only SELECT operation
Attacks that exploit this kind of access
查询表中的数据. It is categorised as a Read tool in the PySqlitMCP MCP Server, which means it retrieves data without modifying state.
Register the PySqlit MCP server in PolicyLayer and add a rule for select_data: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches PySqlitMCP. Nothing to install.
select_data 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 select_data rule in your PolicyLayer 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 PolicyLayer policy for select_data. 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.
select_data is provided by the PySqlit MCP server (python51888/pysqlitmcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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