replay_supervisory_scenario
Supervisory Scenario Replay (DFAST-lite): OpenChainGraph compute node (capital_assessment). Regulatory deadline: 2027-02-01 (Annual re-pin — Fed publishes new supervisory scenarios each February). Deterministic OpenChainGraph compute node. By default (compute:"auto") inputs are computed server-si...
This record as markdown: /tools/postoaklabs-ainumbers-mcp-apps/replay-supervisory-scenario.md
What replay_supervisory_scenario does on Ainumbers Mcp Apps
AI agents invoke replay_supervisory_scenario to trigger actions in Ainumbers Mcp Apps. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
| Parameter | Type | Required | Description |
|---|---|---|---|
compute | string | — | Compute mode (v0.4 Compute Binding). "auto" (default) = server for gpu:false nodes with registered kernels; "server" = force server-side; "browser" = always ret |
parent_hashes | array | — | execution_hash values from upstream ChainGraph AP2 artifacts to chain from (sets chain.parent_hashes in the export). |
parent_tool_ids | array | — | tool_id values matching parent_hashes, in the same order. |
policy_parameters | object | — | Input parameters for this tool's decision function. For gpu:false nodes with a registered kernel, these are computed server-side when compute is "auto" or "serv |
Parameters from the server's own tool schema.
Why replay_supervisory_scenario is rated High
Runs regulatory capital stress-test computations on supplied financial inputs via distributed compute nodes.
From the tool's definition compute node, capital_assessment, forces client-side execution, OpenChainGraph
Attacks that exploit this kind of access
The rule that runs replay_supervisory_scenario safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Ainumbers Mcp Apps, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For replay_supervisory_scenario, this is the rule to start with:
replay_supervisory_scenario stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Ainumbers Mcp Apps, apply this rule, and every replay_supervisory_scenario call is checked against it from then on.
Questions about replay_supervisory_scenario
Supervisory Scenario Replay (DFAST-lite): OpenChainGraph compute node (capital_assessment). Regulatory deadline: 2027-02-01 (Annual re-pin — Fed publishes new supervisory scenarios each February). Deterministic OpenChainGraph compute node. By default (compute:"auto") inputs are computed server-side on Cloudflare Workers for gpu:false nodes with a registered kernel; compute:"browser" forces client-side execution and returns a browser delegation URL instead. gpu:true nodes always delegate to the browser. Inputs are processed transiently to compute the response and are not stored, logged, or retained. Use synthetic or anonymised inputs only. Exports an AP2 artifact with execution_hash for chain provenance. Output feeds: sim-01-lcr-nsfr-liquidity-stress-test, sim-03-basel-rwa-scenario-modeler. Open at: https://ainumbers.co/chaingraph/art-370-supervisory-scenario-replay.html FV-status (published/proven/still-trusted for this spec): /fv-status/8b5ae30d812cd234cfb6068c4ce2022f01d10f2a358979b0f0d73421e09d2543.json — a snapshot, not a subscription; this receipt verifies offline regardless of whether that file is ever fetched. It is categorised as a Execute tool in the Ainumbers Mcp Apps MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
replay_supervisory_scenario accepts 4 parameters: compute, parent_hashes, parent_tool_ids, policy_parameters. The full parameter table on this page comes from the server's own tool schema.
Register the Ainumbers Mcp Apps MCP server in PolicyLayer and add a rule for replay_supervisory_scenario: 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 Ainumbers Mcp Apps. Nothing to install.
replay_supervisory_scenario is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the replay_supervisory_scenario 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 replay_supervisory_scenario. 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.
replay_supervisory_scenario is provided by the Ainumbers Mcp Apps MCP server (postoaklabs/ainumbers-mcp-apps). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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