Get stock split history for a stock.
AI agents call get_splits_info_mcp to retrieve information from Agentic AI System with MCP Integration without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves read-only historical financial information about stock splits. It performs a passive data query operation with no side effects—it does not create, modify, delete, execute code, or move money. The lowercase 'get' prefix and 'history' context confirm it is a simple data retrieval function.
From the tool's definition Tool name 'get_splits_info_mcp' and description 'Get stock split history for a stock' indicate a retrieval operation that queries historical stock split data with no modification, deletion, or execution of code/commands.
Attacks that exploit this kind of access
Get stock split history for a stock. It is categorised as a Read tool in the Agentic AI System with MCP Integration MCP Server, which means it retrieves data without modifying state.
Register the Agentic AI System with MCP Integration MCP server in PolicyLayer and add a rule for get_splits_info_mcp: 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 Agentic AI System with MCP Integration. Nothing to install.
get_splits_info_mcp 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 get_splits_info_mcp 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 get_splits_info_mcp. 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.
get_splits_info_mcp is provided by the Agentic AI System with MCP Integration MCP server (pratyush-usc-mba/designing-an-agentic-ai-system-with-mcp-integration). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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