corpusAI Cloud Pricing

LLM token quotes

tokens_quotes

Read-onlyIdempotent

Paid (0.01 USDC per call). Every listed price for one model on one day: each source and hosting provider, USD per million tokens. Rows from the OpenRouter catalog, OpenRouter per-host endpoints (Azure, Amazon Bedrock, Google, Together, DeepInfra, …), the LiteLLM price table and DeepInfra. Includes cache-read price and context length where known; variants such as batch tiers are marked in extra.variant.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`day`	No	YYYY-MM-DD; default latest.
`model`	Yes	Canonical model slug from /tokens/models. Example: deepseek-v3

Output Schema

TableJSON Schema

Name	Required	Description	Default
`day`	No
`code`	No
`unit`	No
`count`	No
`model`	No
`quotes`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 28, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": true,
+  "properties": {
+    "code": {
+      "type": "string"
+    },
+    "count": {
+      "type": "number"
+    },
+    "day": {
+      "type": "string"
+    },
+    "model": {
+      "type": "string"
+    },
+    "quotes": {
+      "items": {
+        "additionalProperties": true,
+        "properties": {
+          "as_of": {
+            "type": "string"
+          },
+          "cache_read_per_m": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "context": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "extra": {
+            "anyOf": [
+              {
+                "additionalProperties": true,
+                "properties": {},
+                "type": "object"
+              },
+              {
+                "type": "null"
+              }
+            ]
+          },
+          "input_per_m": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "output_per_m": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "provider": {
+            "type": "string"
+          },
+          "sku": {
+            "type": "string"
+          },
+          "source": {
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "unit": {
+      "type": "string"
+    }
+  },
+  "type": "object"
+}

First observedSep 26, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Goes well beyond the readOnly/idempotent annotations by disclosing the per-call charge (0.01 USDC), the exact source set (OpenRouter catalog and per-host endpoints, LiteLLM, DeepInfra), and what each row contains (cache-read price, context length, extra.variant for batch tiers). This is the kind of context annotations cannot carry.

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

Conciseness5/5

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

Two dense but front-loaded sentences: cost first, then scope, then source list and row contents. No filler clauses; every phrase conveys a distinct fact.

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

Completeness5/5

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

With an output schema present, return shape need not be explained, yet the description still summarizes row content and source coverage, and adds the payment requirement. Nothing needed to call it correctly is missing.

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 coverage is 100%, so both parameters are already documented (day defaulting to latest, model slug referencing /tokens/models). The description restates the one-model-one-day semantics but adds no format or syntax detail beyond the schema, so baseline 3 applies.

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

Purpose5/5

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

States a specific verb+resource+scope: 'Every listed price for one model on one day' across named sources. An agent can immediately tell this apart from the index-fixing or board siblings because it's the per-model, per-day quote lookup.

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

Usage Guidelines3/5

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

Usage is implied by the description (fetch quotes for a given model/day) and the 0.01 USDC cost signals it is a deliberate paid lookup, but there is no explicit when-to-use or when-not-to-use guidance and no named alternative (e.g. tokens_history vs tokens_fixing).

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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