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Embedded Insurance Pricing Modeller

price_embedded_insurance
Read-onlyIdempotent

Embedded Insurance Pricing Modeller: OpenChainGraph compute node (analytics_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. Open at: https://ainumbers.co/chaingraph/art-366-price-embedded-insurance.html FV-status (published/proven/still-trusted for this spec): /fv-status/8498d0819c941fdb731f9e10f5d93ee929026919cb111a42149821b48b5ac180.json — a snapshot, not a subscription; this receipt verifies offline regardless of whether that file is ever fetched. Output schema: call describe_tool("price_embedded_insurance").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
computeNoCompute mode (v0.4 Compute Binding). "auto" (default) = server for gpu:false nodes with registered kernels; "server" = force server-side; "browser" = always return browser delegation URL. gpu:true nodes always delegate.
parent_hashesNoexecution_hash values from upstream ChainGraph AP2 artifacts to chain from (sets chain.parent_hashes in the export).
parent_tool_idsNotool_id values matching parent_hashes, in the same order.
policy_parametersNoInput 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 "server". See the tool's manifest for field names.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "annual_gwp": {
-      "type": "integer"
-    },
-    "breakeven_loss_ratio_pct": {
-      "type": "number"
-    },
-    "combined_ratio_pct": {
-      "type": "integer"
-    },
-    "commission_cost": {
-      "type": "integer"
-    },
-    "expected_losses": {
-      "type": "integer"
-    },
-    "expense_ratio_pct": {
-      "type": "integer"
-    },
-    "monthly_gwp": {
-      "type": "integer"
-    },
-    "net_written_premium": {
-      "type": "integer"
-    },
-    "note": {
-      "type": "string"
-    },
-    "opex_cost": {
-      "type": "integer"
-    },
-    "per_tx_premium": {
-      "type": "number"
-    },
-    "underwriting_profit": {
-      "type": "integer"
-    }
-  },
-  "type": "object"
-}New value: +null
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "annual_gwp": {
+      "type": "integer"
+    },
+    "breakeven_loss_ratio_pct": {
+      "type": "number"
+    },
+    "combined_ratio_pct": {
+      "type": "integer"
+    },
+    "commission_cost": {
+      "type": "integer"
+    },
+    "expected_losses": {
+      "type": "integer"
+    },
+    "expense_ratio_pct": {
+      "type": "integer"
+    },
+    "monthly_gwp": {
+      "type": "integer"
+    },
+    "net_written_premium": {
+      "type": "integer"
+    },
+    "note": {
+      "type": "string"
+    },
+    "opex_cost": {
+      "type": "integer"
+    },
+    "per_tx_premium": {
+      "type": "number"
+    },
+    "underwriting_profit": {
+      "type": "integer"
+    }
+  },
+  "type": "object"
+}
  3. Added

TDQS

B3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the bar is lower, and the description adds real value beyond them: transient/non-retained input processing, deterministic execution, server-vs-browser compute delegation, and an AP2 export carrying execution_hash for provenance. This is substantive behavioral context the agent would not get from the structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The runtime/provenance content is dense and reasonably front-loaded, but the long promotional URL and the full 64-character FV-status hash path are noise for tool selection. Several sentences (the snapshot-not-subscription aside, the offline-verification note) do not help an agent decide or invoke the tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description should carry return-value explanation; it only notes the AP2 artifact with execution_hash and defers the real output shape to describe_tool. Combined with policy_parameters field names deferred to an unstated manifest, an agent knows how to run the node but not what the pricing function actually yields.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all four parameters and the baseline is 3. The description restates the compute modes (largely duplicating the schema enum) and hints at chaining via execution_hash, but it explicitly punts on the important policy_parameters field names ("See the tool's manifest"), adding little beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence identifies the resource (embedded insurance pricing) and the artifact class (OpenChainGraph compute node), so an agent knows roughly what it is. However it never says what the model actually prices or returns, and it does nothing to distinguish the tool from insurance-adjacent siblings such as run_insurance_reporting_fit or lint_insurance_evidence_freshness. The purpose is implied rather than stated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a constraint ("Use synthetic or anonymised inputs only") but no when-to-use, when-not-to-use, or alternative-tool guidance relative to the many sibling analytics tools. An agent cannot infer from the text when this node should be selected over a competing pricing or fit tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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