Low Risk

check_feasibility

Check whether a set of constraints can be simultaneously satisfied. Uses the twist-compression operator to detect structural obstructions — fundamental conflicts in the constraint system that cannot be resolved by adjusting any single field. Returns obstruction type, magnitude, and the specific c...

Risk signalsHandles credentials or secrets (api_key)

Part of the Governance Platform server.

check_feasibility is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

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AI agents call check_feasibility to retrieve information from Governance Platform 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 check_feasibility 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.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "check_feasibility": {}
  }
}

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These attack patterns abuse exactly the kind of access check_feasibility gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so check_feasibility only ever does what you allow.

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Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the check_feasibility tool do? +

Check whether a set of constraints can be simultaneously satisfied. Uses the twist-compression operator to detect structural obstructions — fundamental conflicts in the constraint system that cannot be resolved by adjusting any single field. Returns obstruction type, magnitude, and the specific constraint interactions causing the conflict. Fast pre-check before running full validation. Args: api_key: GeodesicAI API key (starts with gai_) constraints: List of derivation rules and formal constraints to check field_values: Current field values (numeric key-value pairs). It is categorised as a Read tool in the Governance Platform MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on check_feasibility? +

Register the Governance Platform MCP server in PolicyLayer and add a rule for check_feasibility: 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 Governance Platform. Nothing to install.

What risk level is check_feasibility? +

check_feasibility is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit check_feasibility? +

Yes. Add a rate_limit block to the check_feasibility 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.

How do I block check_feasibility completely? +

Set action: deny in the PolicyLayer policy for check_feasibility. 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 check_feasibility? +

check_feasibility is provided by the Governance Platform MCP server (https://app.geodesiclabs.ai/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Governance Platform tool call.

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