Predict variant effects across multiple tissues. Compares regulatory impact in different tissues to identify tissue-specific effects. Default tissues: brain, liver, heart (customizable) Returns impact levels and expression changes for each tissue. Perfect for: understanding tissue-specific diseas...
AI agents call predict_tissue_specific to retrieve information from AlphaGenome MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool performs read/query operations: it takes variant input, runs predictive analysis across tissues, and returns results. No data is written, deleted, executed, or financially committed. The description emphasizes returning information ('Returns impact levels and expression changes'). Severity is low because misuse would at worst produce misleading genomic predictions, not system-level harm.
From the tool's definition 'Predict variant effects across multiple tissues' and 'Returns impact levels and expression changes for each tissue' — the tool computes and returns predictions/analysis with no indication of data modification or side effects.
Attacks that exploit this kind of access
Predict variant effects across multiple tissues. Compares regulatory impact in different tissues to identify tissue-specific effects. Default tissues: brain, liver, heart (customizable) Returns impact levels and expression changes for each tissue. Perfect for: understanding tissue-specific disease mechanisms, prioritizing relevant tissues. Example:. It is categorised as a Read tool in the AlphaGenome MCP Server MCP Server, which means it retrieves data without modifying state.
Register the AlphaGenome MCP Server MCP server in PolicyLayer and add a rule for predict_tissue_specific: 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 AlphaGenome MCP Server. Nothing to install.
predict_tissue_specific 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 predict_tissue_specific 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 predict_tissue_specific. 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.
predict_tissue_specific is provided by the AlphaGenome MCP Server MCP server (taehojo/alphagenome-mcp). 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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