Agent-Service Metering & Marketplace Economics Modeler — OpenChainGraph compute node (payment_policy). Runs deterministically in-browser; zero PII, zero egress. Exports an AP2 artifact with execution_hash for chain provenance. Consumes upstream artifacts from: art-60-agent-economy-runtime-fit-dia...
AI agents invoke model_agent_service_metering 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.
The tool executes a deterministic compute process (metering and marketplace economics modeling), consumes upstream artifacts, and produces downstream AP2 artifacts with execution hashes for chain provenance.
From the tool's definition 'Runs deterministically in-browser', 'Exports an AP2 artifact with execution_hash for chain provenance', 'Consumes upstream artifacts', 'Output feeds' downstream tools — it executes a modeling computation and produces chained artifacts
Attacks that exploit this kind of access
Agent-Service Metering & Marketplace Economics Modeler — OpenChainGraph compute node (payment_policy). Runs deterministically in-browser; zero PII, zero egress. Exports an AP2 artifact with execution_hash for chain provenance. Consumes upstream artifacts from: art-60-agent-economy-runtime-fit-diagnostic. Output feeds: art-03-x402-settlement-modeler, ml-03-timeseries-anomaly-detector. Open at: https://ainumbers.co/chaingraph/art-63-agent-service-metering-modeler.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.
model_agent_service_metering 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 model_agent_service_metering: 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.
model_agent_service_metering 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 model_agent_service_metering 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 model_agent_service_metering. 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.
model_agent_service_metering 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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