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Get Talonic Pricing

talonic_get_pricing
Read-only

Check fixed per-unit rates to estimate costs for planned extraction, structuring, or matching jobs before spending credits.

Instructions

Read Talonic's machine-readable credit pricing catalog: fixed per-unit rates so you can predict spend BEFORE running anything.

USE WHEN: estimating the cost of a planned extraction/structuring/matching job, or answering a pricing question. Public — works without spending credits. NOT FOR: the workspace's current balance (use talonic_get_balance) or what it has already spent (use talonic_get_usage). ARGS: none. RETURNS: currency, credits_per_eur, multipliers (e.g. batch 0.5x), and units[] — each { unit, label, credits, eur, free }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
currencyYesBilling currency (always EUR).
credits_per_eurYesCredits per EUR (e.g. 1000 = €1).
multipliersYesProcessing-mode multipliers applied on top of per-unit cost (e.g. { realtime: 1, batch: 0.5 }).
unitsYesThe per-unit pricing catalog.
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by stating it is public and does not spend credits, which is consistent with the annotations. No contradictions.

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?

The description is concise and well-structured with clear headings (USE WHEN, NOT FOR, ARGS, RETURNS). Every sentence is informative and earns its place. No wasted words.

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?

Given zero parameters and an output schema (though not shown), the description fully covers what the agent needs: purpose, usage, and return format. The annotations provide safety context, making it complete.

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?

With zero parameters and 100% schema coverage, baseline is 4. The description mentions 'ARGS: none' and explains the return structure (currency, credits_per_eur, etc.), which adds meaning beyond the empty schema.

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 uses a specific verb 'Read' and identifies the resource 'Talonic's machine-readable credit pricing catalog'. It clearly distinguishes from sibling tools like talonic_get_balance and talonic_get_usage by stating its purpose is for estimating costs.

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

Usage Guidelines5/5

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

Explicitly provides use cases ('USE WHEN: estimating cost'), non-use cases ('NOT FOR: balance/usage'), and names alternative tools. This leaves no ambiguity about when to invoke this 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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