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Run Agent AVCP audition

run_ai_pack_audition

Validate an AI Pack version against expected points and version by running integrity, model, regression, memory-isolation, injection, growth, and cost gates after explicit confirmation.

Instructions

Run version-bound integrity, model, regression, memory-isolation, injection, growth, and cost gates after explicit Point confirmation.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "productId": "<productId>",
      "expectedPoints": "<expectedPoints>",
      "expectedVersion": "<expectedVersion>",
      "confirmation": "<confirmation>"
    },
    "name": "run_ai_pack_audition"
  }
}

Input Schema

Name	Required	Description	Default
`tier`	No		verified_listing
`productId`	Yes	Tetrees AI Pack product id
`confirmation`	Yes
`expectedPoints`	Yes
`expectedVersion`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.2.1Sep 7, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does reveal that multiple gate types are executed and that confirmation is required, but it does not state whether running the audition mutates pack state, incurs cost, has long runtime, or how gate failures are reported.

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 dense, verb-first sentence with no filler, and the precondition is embedded reasonably early. The heavy jargon and long unbroken list of gate types reduce scannability, but the size is still appropriate for the content.

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?

For a tool with five parameters, no output schema, and no annotations, the description is thin: it omits return values, failure behavior, side effects, and how to set expectedPoints or expectedVersion. It provides only the confirmation precondition and gate scope, so an agent cannot confidently invoke it correctly.

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 20%, so the description must compensate but largely does not. 'Version-bound' loosely maps to expectedVersion and 'confirmation' aligns with the confirmation parameter, but expectedPoints, tier, and the confirmation constant are not semantically explained.

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 specifies a clear action ('Run'), a resource ('AI pack audition'), and a distinct set of gate categories, which separates it from generic run tools. However, 'Point confirmation' and 'AVCP' are unexplained, and the description does not say what the tool returns, so clarity is good but not complete.

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

Usage Guidelines3/5

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

The phrase 'after explicit Point confirmation' implies a precondition, and 'version-bound' hints at when the tool applies. But it gives no explicit guidance on when to use this tool versus siblings like quote_ai_pack_audition, submit_ai_pack_for_audition, or run_ai_pack, and lists no exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.