model_buy_in_exposure

SERVERAinumbers Mcp Apps SOURCEpostoaklabs/ainumbers-mcp-apps

High RISK CLASS

Category Execute

Parameters 40 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/postoaklabs-ainumbers-mcp-apps/model-buy-in-exposure.md

WHAT IT DOES

What model_buy_in_exposure does on Ainumbers Mcp Apps

AI agents invoke model_buy_in_exposure 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.

RISK

Why model_buy_in_exposure is rated High

Executes deterministic financial exposure modeling computation on cloud or browser infrastructure.

From the tool's definition OpenChainGraph compute node, deterministic, exports AP2 artifact with execution_hash

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs model_buy_in_exposure 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 model_buy_in_exposure, this is the rule to start with:

model_buy_in_exposure Rate-limited

model_buy_in_exposure 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "model_buy_in_exposure": {
      "limits": [
        {
          "counter": "model_buy_in_exposure_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Ainumbers Mcp Apps, apply this rule, and every model_buy_in_exposure call is checked against it from then on.

FAQ

Questions about model_buy_in_exposure

What does the model_buy_in_exposure tool do? +

Buy-In Exposure Modeler: OpenChainGraph compute node (compliance_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. Consumes upstream artifacts from: art-78-csdr-penalty-calculator. Output feeds: cry-05-agent-action-audit-trail-aggregator. Open at: https://ainumbers.co/chaingraph/art-83-buy-in-exposure-modeler.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.

What parameters does model_buy_in_exposure accept? +

model_buy_in_exposure 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.

How do I enforce a policy on model_buy_in_exposure? +

Register the Ainumbers Mcp Apps MCP server in PolicyLayer and add a rule for model_buy_in_exposure: 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.

What risk level is model_buy_in_exposure? +

model_buy_in_exposure is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit model_buy_in_exposure? +

Yes. Add a rate_limit block to the model_buy_in_exposure 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.

How do I block model_buy_in_exposure completely? +

Set action: deny in the PolicyLayer policy for model_buy_in_exposure. 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.

What MCP server provides model_buy_in_exposure? +

model_buy_in_exposure 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.

KEEP EXPLORING

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