Query the multi-model AI router. If a prompt is provided, the prompt is sent to the routed model. Otherwise, returns only the routing decision. Supported providers: OpenAI, Anthropic, Sarvam AI, Ollama. Args: - task_type (string): multilingual | reasoning | local | general - prompt (string, optio...
AI agents invoke neuroverse_model to trigger actions in Neuroverse. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool routes and executes prompts against external AI model providers. While it primarily returns information, it triggers external API calls whose nature and effects depend entirely on the prompt content. It spans Read and Execute; since it dispatches arbitrary prompts to external systems (potentially causing downstream effects), Execute is the most accurate category.
From the tool's definition "Query the multi-model AI router" and "the prompt is sent to the routed model" — triggers external operations against AI providers (OpenAI, Anthropic, Sarvam AI, Ollama) whose effects depend on the prompt argument
Attacks that exploit this kind of access
Query the multi-model AI router. If a prompt is provided, the prompt is sent to the routed model. Otherwise, returns only the routing decision. Supported providers: OpenAI, Anthropic, Sarvam AI, Ollama. Args: - task_type (string): multilingual | reasoning | local | general - prompt (string, optional): Prompt to send Returns: JSON with routing decision and optional model response. It is categorised as a Execute tool in the Neuroverse MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Neuroverse MCP server in PolicyLayer and add a rule for neuroverse_model: 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 Neuroverse. Nothing to install.
neuroverse_model is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the neuroverse_model 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 neuroverse_model. 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.
neuroverse_model is provided by the Neuroverse MCP server (joshua400/neuroverse). 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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