Quantize f32 neural network weights to ternary {-1, 0, +1} using BitNet-style absolute-mean thresholding. Returns quantized weights, sparsity ratio, and effective compute savings.
Part of the Ternary Intelligence Stack MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents call quantize_weights to retrieve information from Ternary Intelligence Stack without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though quantize_weights only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
tools:
quantize_weights:
rules:
- action: allowSee the full Ternary Intelligence Stack policy for all 30 tools.
Agents calling read-class tools like quantize_weights have been implicated in these attack patterns. Read the full case and prevention policy for each:
Other tools in the Read risk category across the catalogue. The same policy patterns (rate-limit, allow) apply to each.
Quantize f32 neural network weights to ternary {-1, 0, +1} using BitNet-style absolute-mean thresholding. Returns quantized weights, sparsity ratio, and effective compute savings.. It is categorised as a Read tool in the Ternary Intelligence Stack MCP Server, which means it retrieves data without modifying state.
Add a rule in your Intercept YAML policy under the tools section for quantize_weights. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Ternary Intelligence Stack MCP server.
quantize_weights 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 quantize_weights rule in your Intercept 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 Intercept policy for quantize_weights. 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.
quantize_weights is provided by the Ternary Intelligence Stack MCP server (rfi-irfos/ternlang). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Open source. One binary. Zero dependencies.
npx -y @policylayer/intercept