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corpusAI Cloud Pricing

LLM models with token prices

tokens_models
Read-onlyIdempotent

Free. Every LLM with a token price fixing, its ticker codes and today's price range. Canonical model slugs (for example claude-sonnet-5, gpt-6-astra, llama-4-maverick), their ticker codes, how many sources and hosts list them, and the min/max input and output price seen today. Also lists the frontier basket.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dayNo
unitNo
countNo
fixingNo
modelsNo
methodologyNo
frontier_basketNo
frontier_tickersNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "$schema": "http://json-schema.org/draft-07/schema#",
      +  "additionalProperties": true,
      +  "properties": {
      +    "count": {
      +      "type": "number"
      +    },
      +    "day": {
      +      "type": "string"
      +    },
      +    "fixing": {
      +      "type": "string"
      +    },
      +    "frontier_basket": {
      +      "items": {
      +        "type": "string"
      +      },
      +      "type": "array"
      +    },
      +    "frontier_tickers": {
      +      "items": {
      +        "type": "string"
      +      },
      +      "type": "array"
      +    },
      +    "methodology": {
      +      "type": "string"
      +    },
      +    "models": {
      +      "items": {
      +        "additionalProperties": true,
      +        "properties": {
      +          "code": {
      +            "type": "string"
      +          },
      +          "input_hi": {
      +            "type": [
      +              "number",
      +              "null"
      +            ]
      +          },
      +          "input_lo": {
      +            "type": [
      +              "number",
      +              "null"
      +            ]
      +          },
      +          "model": {
      +            "type": "string"
      +          },
      +          "output_hi": {
      +            "type": [
      +              "number",
      +              "null"
      +            ]
      +          },
      +          "output_lo": {
      +            "type": [
      +              "number",
      +              "null"
      +            ]
      +          },
      +          "providers": {
      +            "type": "number"
      +          },
      +          "rows": {
      +            "type": "number"
      +          },
      +          "sources": {
      +            "type": "number"
      +          },
      +          "tickers": {
      +            "items": {
      +              "type": "string"
      +            },
      +            "type": "array"
      +          }
      +        },
      +        "type": "object"
      +      },
      +      "type": "array"
      +    },
      +    "unit": {
      +      "type": "string"
      +    }
      +  },
      +  "type": "object"
      +}
  2. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already cover readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is fully declared. The description adds useful context beyond that: the data is free, it is a daily snapshot ('today's price range'), and it includes provenance counts (how many sources and hosts list each model). It does not discuss caching or refresh behavior, but the remaining gaps are minor.

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

Conciseness4/5

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

Front-loaded with 'Free.' and then a single dense sentence enumerating the returned fields, followed by a short note about the frontier basket. No filler, though the middle sentence is long and comma-heavy, which slightly reduces scannability.

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

Completeness4/5

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

With an output schema present, the description need not document return structure, yet it does so helpfully; combined with the read-only annotations and zero parameters, an agent has enough to invoke it correctly. The main omission is any indication of which sibling tool to use for other token-price needs.

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

Parameters4/5

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

The tool takes no parameters (0 params, no required fields), so the baseline is 4. The description correctly implies a parameterless bulk listing rather than a filtered query, which is consistent with the empty schema.

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

Purpose4/5

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

The description clearly states what the tool returns: canonical model slugs, ticker codes, source/host counts, and today's min/max input/output prices, plus the frontier basket. It is unambiguous what resource is being listed. However, it never distinguishes itself from close siblings like tokens_board, tokens_fixing, tokens_quotes, or tokens_history, which all deal with the same token-price domain.

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?

There is no when-to-use or when-not-to-use guidance, and no alternative is named. 'Free' hints that cost is not a concern but gives no routing logic. An agent must infer from the name alone whether this is the model catalog versus the board/fixing/quotes siblings.

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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