propose_outcome
Propose winning outcome (5 USDB bond).
This record as markdown: /tools/launch-on-basis-mcp-ts/propose-outcome.md
What propose_outcome does on Basis MCP Server
AI agents use propose_outcome to commit financial operations through Basis MCP Server, usually the final step of a payment, billing, or trading workflow. A call moves real money.
Why propose_outcome is rated Critical
This tool commits a financial obligation by posting a 5 USDB bond to propose an outcome in what appears to be a prediction market. It involves real financial assets (USDB tokens as collateral/bond), making it a Financial category action. Misuse could result in loss of the bonded funds or manipulation of prediction market outcomes.
From the tool's definition Propose winning outcome (5 USDB bond)
Attacks that exploit this kind of access
The rule that runs propose_outcome safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Basis MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For propose_outcome, this is the rule to start with:
Any call to propose_outcome is blocked until a human approves it. The rest of the server keeps working.
The button opens the PolicyLayer dashboard: create your workspace, connect Basis MCP Server, apply this rule, and every propose_outcome call is checked against it from then on.
Questions about propose_outcome
Propose winning outcome (5 USDB bond). It is categorised as a Financial tool in the Basis MCP Server MCP Server, which means it involves financial transactions. Block by default and require explicit approval.
Register the Basis MCP Server MCP server in PolicyLayer and add a rule for propose_outcome: 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 Basis MCP Server. Nothing to install.
propose_outcome is a Financial tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the propose_outcome 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 propose_outcome. 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.
propose_outcome is provided by the Basis MCP Server MCP server (launch-on-basis/mcp-ts). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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