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TetreesEX

Tetrees AI

Official
by TetreesEX

Quote Agent AVCP audition

quote_ai_pack_audition

Get exact version-bound Point quotes and Agent AVCP deliverables by providing a Tetrees AI Pack product ID.

Instructions

Return exact version-bound Point quotes and Agent AVCP deliverables.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
productIdYesTetrees AI Pack product id

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv2.2.1

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. 'Return' suggests a read-like operation, but the description does not explain prerequisites, side effects, whether an audition must already exist, or how version binding behaves. This is a significant gap for a tool with no annotation safety hints.

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 a single well-structured sentence with no filler words, and the main return object is front-loaded. It loses a point because the undefined acronym 'AVCP' and the jargon 'Point quotes' make the sentence dense and cryptic, though still compact.

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 no output schema, no annotations, and no usage guidance, the description does not fully equip an agent to invoke the tool correctly. It identifies what is returned but not when to use it, what 'AVCP deliverables' mean, whether an audition must be run first, or what the returned data structure looks like. The presence of many related siblings increases the need for more context.

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?

The input schema already describes the only parameter, productId, with 100% coverage. The description does not add additional meaning about parameter usage beyond tying the action to version-bound quotes. Per the baseline for high schema coverage, score 3 is appropriate.

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 uses a clear verb ('Return') and a specific resource ('exact version-bound Point quotes and Agent AVCP deliverables'), so it states what the tool produces. It is not a tautology, and the 'version-bound' qualifier adds specificity. However, it does not explicitly distinguish itself from the similar sibling quote_agent_run.

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?

No guidance is given about when to use this tool versus alternatives such as quote_agent_run, run_ai_pack_audition, or get_ai_pack_report. The description implies the tool returns audition-related quotes, but it never states the conditions that should lead an agent to select it.

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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