Analyzes code snippets by comparing them with best practices from official documentation found via web search. Identifies potential bugs, performance issues, and security vulnerabilities. Uses the configured Vertex AI model (${modelIdPlaceholder}) with Google Search. Requires
AI agents call code_analysis_with_docs to retrieve information from Google AI Search MCP without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves documentation via web search and performs static analysis/comparison of provided code snippets. It produces advisory output (identifying issues) but does not execute code, create/modify systems, delete data, or move money. The analysis is purely informational with no side effects on external systems.
From the tool's definition Tool performs 'analyzes code snippets by comparing them with best practices' and 'identifies potential bugs, performance issues, and security vulnerabilities' — all informational/analytical operations with no described capability to modify, execute, delete,…
Attacks that exploit this kind of access
Analyzes code snippets by comparing them with best practices from official documentation found via web search. Identifies potential bugs, performance issues, and security vulnerabilities. Uses the configured Vertex AI model (${modelIdPlaceholder}) with Google Search. Requires. It is categorised as a Read tool in the Google AI Search MCP MCP Server, which means it retrieves data without modifying state.
Register the Google AI Search MCP server in PolicyLayer and add a rule for code_analysis_with_docs: 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 Google AI Search MCP. Nothing to install.
code_analysis_with_docs 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 code_analysis_with_docs 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 code_analysis_with_docs. 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.
code_analysis_with_docs is provided by the Google AI Search MCP server (shariqriazz/google-ai-search-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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