Recommend ranked target journals for a paper from a ~1,200-venue
AI agents call recommend_journals to retrieve information from Science Ai without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries a journal database to provide recommendations—a read-only operation that retrieves and presents information. There is no data creation, modification, deletion, code execution, or financial transaction involved. The worst case of misuse would be receiving an inappropriate journal recommendation, which has negligible impact. This is a straightforward Read category tool with low severity.
From the tool's definition Tool description states it 'Recommend ranked target journals for a paper from a ~1,200-venue' database. The verb 'recommend' and the action of retrieving and ranking journals from an existing catalogue indicates data retrieval with no modifications,…
Attacks that exploit this kind of access
Recommend ranked target journals for a paper from a ~1,200-venue. It is categorised as a Read tool in the Science Ai MCP Server, which means it retrieves data without modifying state.
Register the Science Ai MCP server in PolicyLayer and add a rule for recommend_journals: 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 Science Ai. Nothing to install.
recommend_journals 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 recommend_journals 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 recommend_journals. 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.
recommend_journals is provided by the Science Ai MCP server (selfpy/science-ai-mcp-server). 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.
Teams ship this data inside their own products. See what a licence covers →