Agentic Payments Readiness Diagnostic — OpenChainGraph compute node (agent_guardrail_mandate). Runs deterministically in-browser; zero PII, zero egress. Exports an AP2 artifact with execution_hash for chain provenance. Output feeds: art-15-agentic-mandate-sandbox, art-16-google-ap2-mandate-builde...
AI agents invoke run_agentic_readiness_diagnostic 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.
This tool executes a diagnostic computation and produces an exportable artifact with a hash for chain provenance, making it Execute rather than Read. While the server description claims read-only/zero PII, the tool actively runs a process and generates outputs that feed downstream systems.
From the tool's definition 'Runs deterministically in-browser', 'Exports an AP2 artifact with execution_hash for chain provenance', 'OpenChainGraph compute node (agent_guardrail_mandate)'
Attacks that exploit this kind of access
Agentic Payments Readiness Diagnostic — OpenChainGraph compute node (agent_guardrail_mandate). Runs deterministically in-browser; zero PII, zero egress. Exports an AP2 artifact with execution_hash for chain provenance. Output feeds: art-15-agentic-mandate-sandbox, art-16-google-ap2-mandate-builder, art-17-ap2-mcp-policy-validator, art-18-mcp-developer-readiness-scorecard. Open at: https://ainumbers.co/chaingraph/art-27-agentic-readiness-diagnostic.html. 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_agentic_readiness_diagnostic 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_agentic_readiness_diagnostic: 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_agentic_readiness_diagnostic 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_agentic_readiness_diagnostic 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_agentic_readiness_diagnostic. 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_agentic_readiness_diagnostic 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.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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