Medium Risk

burnrate_optimize

Get a cheaper equivalent plan by substituting models with lower-cost alternatives. Call after burnrate_estimate if the estimated cost exceeds your budget. Returns the optimized plan with substituted models, new per-step costs, total savings, and whether the target_budget is met. Optionally set ta...

Part of the Plith MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.

plith/plith Write

AI agents use burnrate_optimize to create or modify resources in Plith. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call burnrate_optimize repeatedly, creating or modifying resources faster than any human could review. Intercept's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Plith.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

plith-plith.yaml
tools:
  burnrate_optimize:
    rules:
      - action: allow
        rate_limit:
          max: 30
          window: 60

See the full Plith policy for all 14 tools.

Tool Name burnrate_optimize
Category Write
MCP Server Plith MCP Server
Risk Level Medium

View all 14 tools →

Agents calling write-class tools like burnrate_optimize have been implicated in these attack patterns. Read the full case and prevention policy for each:

Browse the full MCP Attack Database →

Other tools in the Write risk category across the catalogue. The same policy patterns (rate-limit, validate) apply to each.

What does the burnrate_optimize tool do? +

Get a cheaper equivalent plan by substituting models with lower-cost alternatives. Call after burnrate_estimate if the estimated cost exceeds your budget. Returns the optimized plan with substituted models, new per-step costs, total savings, and whether the target_budget is met. Optionally set target_budget to constrain the optimization.. It is categorised as a Write tool in the Plith MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on burnrate_optimize? +

Add a rule in your Intercept YAML policy under the tools section for burnrate_optimize. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Plith MCP server.

What risk level is burnrate_optimize? +

burnrate_optimize is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit burnrate_optimize? +

Yes. Add a rate_limit block to the burnrate_optimize 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.

How do I block burnrate_optimize completely? +

Set action: deny in the Intercept policy for burnrate_optimize. 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.

What MCP server provides burnrate_optimize? +

burnrate_optimize is provided by the Plith MCP server (plith/plith). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Let agents act without letting them run wild.

Deterministic policy on every MCP tool call. Per-identity grants. Full audit log.

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