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Quote an AI Pack run

quote_agent_run
Read-only

Get the exact worst-case Point reservation before using platform-funded inference. BYOK quotes zero model points.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes
productIdYesTetrees AI Pack product id
fundingModeNopoints
enabledSkillsNo
growthVersionNoHosted intelligence checkpoint. Omit to use the account default; 0 runs the signed base Pack.
maxInputTokensNo
maxOutputTokensNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description is not burdened with basic safety disclosure. It adds useful behavioral context by explaining the 'exact worst-case' nature of the quote and that BYOK mode returns zero points, but it does not detail other behaviors such as failure modes, quota constraints, or what happens with invalid inputs.

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 short sentences deliver the essential purpose and a key special case with no filler. The most important information is front-loaded, and every word earns its place.

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

Completeness2/5

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

With 7 parameters, no output schema, and low schema coverage, the description is not complete enough for an agent to confidently invoke the tool correctly. It explains the high-level quote purpose and BYOK behavior but offers no guidance on required parameters, return format, or constraints around token limits and skills.

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

Parameters2/5

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

Schema description coverage is only 29%, so the description should compensate for the many undocumented parameters. It does not explain model, productId, enabledSkills, maxInputTokens, maxOutputTokens, or growthVersion beyond the schema. The only parameter-related insight is the BYOK/funding-mode distinction, which is not named explicitly and is insufficient for a 7-parameter tool.

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 a specific verb ('Get') and resource ('worst-case Point reservation') and ties it to platform-funded inference, which makes the tool's purpose immediately understandable. It does not explicitly distinguish itself from the sibling 'quote_ai_pack_audition', but the wording 'AI Pack run' provides reasonable differentiation.

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 gives clear context for when to use the tool: before using platform-funded inference, to obtain a Point reservation. It also notes the BYOK condition where the quote is zero points. However, it does not explicitly state when not to use it or name alternatives, so it stops short of a 5.

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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