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corpusAI Cloud Pricing

LLM token fixing

tokens_fixing
Read-onlyIdempotent

Paid (0.01 USDC per call). One day's token price fixing for a ticker such as CX.TOK.CLAUDESONNET5.IN. Median across every source and host at the standard tier (batch and free variants excluded). .IN and .OUT are input and output USD per million tokens; .BLEND is (3·input + output) / 4. CX.TOK.FRONTIER.<side> is the median across the frontier basket listed by /tokens/models.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dayNoYYYY-MM-DD; default latest.
tickerYesCX.TOK.<MODELCODE>.IN | OUT | BLEND, or CX.TOK.FRONTIER.<side>. Example: CX.TOK.FRONTIER.BLEND

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it is a paid call (0.01 USDC per call), excludes batch/free variants, and defines the median calculation across sources and hosts. This goes beyond the annotations.

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?

The description is compact and front-loaded with the most important operational fact (paid call), then explains the ticker semantics. Every sentence earns its place, though the ticker explanation is dense and could be slightly clearer.

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?

For a read-only lookup tool with 2 params and full schema coverage, the description covers the key context: cost, exclusions, ticker semantics, and median methodology. It doesn't describe the return format, but there is no output schema and the tool is simple enough that the return shape is likely self-evident from the ticker semantics.

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 both parameters. The description adds meaning by explaining the ticker syntax and the .IN/.OUT/.BLEND semantics, which is helpful, but it doesn't add much beyond what the schema's example already conveys. Baseline 3 is appropriate.

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?

The description states a specific verb+resource: it fixes one day's token price for a ticker like CX.TOK.CLAUDESONNET5.IN, and explains the ticker naming convention. It distinguishes itself from siblings like tokens_history and tokens_quotes by focusing on the daily fixing/median across sources.

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

Usage Guidelines4/5

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

The description explains the ticker format and the meaning of .IN/.OUT/.BLEND and FRONTIER, which tells the agent how to construct the required parameter. It doesn't explicitly say when to use this vs tokens_history/tokens_quotes, but the 'one day's token price fixing' framing implies a daily snapshot use case.

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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