run_sanctions_screening_fit
Sanctions & Export-Control Screening Fit Diagnostic: OpenChainGraph compute node (agent_guardrail_mandate). 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" ...
This record as markdown: /tools/postoaklabs-ainumbers-mcp-apps/run-sanctions-screening-fit.md
What run_sanctions_screening_fit does on Ainumbers Mcp Apps
AI agents invoke run_sanctions_screening_fit 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 run_sanctions_screening_fit is rated High
Executes deterministic sanctions screening computation with configurable backend execution model and external delegation.
From the tool's definition compute node, server-side execution on Cloudflare Workers, browser delegation, GPU processing
Attacks that exploit this kind of access
The rule that runs run_sanctions_screening_fit 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 run_sanctions_screening_fit, this is the rule to start with:
run_sanctions_screening_fit 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 run_sanctions_screening_fit call is checked against it from then on.
Questions about run_sanctions_screening_fit
Sanctions & Export-Control Screening Fit Diagnostic: OpenChainGraph compute node (agent_guardrail_mandate). 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: art-91-ownership-50pct-aggregator, art-92-screening-list-coverage-checker, art-93-fuzzy-match-calibration-scorer, art-94-eccn-dual-use-classifier, art-95-circumvention-diligence-assessor, cry-05-agent-action-audit-trail-aggregator. Open at: https://ainumbers.co/chaingraph/art-90-sanctions-screening-fit-diagnostic.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.
run_sanctions_screening_fit 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 run_sanctions_screening_fit: 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.
run_sanctions_screening_fit 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 run_sanctions_screening_fit 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 run_sanctions_screening_fit. 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.
run_sanctions_screening_fit 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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