[Premium] Solve linear / mixed-integer / quadratic programs (HiGHS solver). Use when the objective and constraints are linear (or quadratic) and you need a provably optimal solution: budget allocation across line items, supply chain optimization, capacity planning with integer counts, portfolio c...
Risk signalsHigh parameter count (12 properties)
Part of the Oraclaw server.
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AI agents use solve_constraints to create or modify resources in Oraclaw. 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 solve_constraints repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Oraclaw.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"solve_constraints": {
"limits": [
{
"counter": "solve_constraints_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Oraclaw policy for all 17 tools.
These attack patterns abuse exactly the kind of access solve_constraints gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
[Premium] Solve linear / mixed-integer / quadratic programs (HiGHS solver). Use when the objective and constraints are linear (or quadratic) and you need a provably optimal solution: budget allocation across line items, supply chain optimization, capacity planning with integer counts, portfolio construction with hard caps. For continuous black-box objectives, use optimize_cmaes. For task→slot scheduling, use solve_schedule. Returns variable assignments + objective value. Requires ORACLAW_API_KEY.. It is categorised as a Write tool in the Oraclaw MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Oraclaw MCP server in PolicyLayer and add a rule for solve_constraints: 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 Oraclaw. Nothing to install.
solve_constraints is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the solve_constraints 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 solve_constraints. 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.
solve_constraints is provided by the Oraclaw MCP server (@oraclaw/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 17 Oraclaw tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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