run_ai_pack_auditionRun version-bound integrity, model, regression, memory-isolation, injection, growth, and cost gates after explicit Point confirmation.
| Name | Required | Description | Default |
|---|---|---|---|
| tier | No | verified_listing | |
| productId | Yes | Tetrees AI Pack product id | |
| confirmation | Yes | ||
| expectedPoints | Yes | ||
| expectedVersion | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint false and destructiveHint false, the tool may mutate state, but the description does not disclose side effects, whether the gates are read-only checks, or what happens after the run.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single compact sentence with no filler, but the dense list of gate categories makes it less scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex audition tool with five parameters and no output schema, the description omits what the gates verify, what a successful or failed audition means, and what output or side effects to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 20% (only productId is described). The description uses jargon like 'version-bound' and 'Point confirmation' without mapping it to expectedVersion, expectedPoints, or confirmation, so it does not compensate for the missing parameter docs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description gives a specific verb ('Run') and names the resource ('Agent AVCP audition'), and enumerates categories of gates, but leaves 'AVCP' and 'Point confirmation' undefined, so the intended purpose remains partially opaque.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to choose this over sibling tools such as run_ai_pack, submit_ai_pack_for_audition, or quote_ai_pack_audition; 'after explicit Point confirmation' is the only contextual hint.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinct by action and resource, though several clusters (accept/propose/select/list growth, prepare/execute/run) require careful reading of descriptions to avoid confusion.
All tool names follow a consistent verb_noun snake_case pattern, with clear action prefixes and descriptive resource nouns.
With 26 tools, the set exceeds the recommended range and feels heavy, even though the domain covers publishing, runtime, growth, and reporting workflows.
The toolkit covers the main AI pack lifecycle—authoring, publishing, acquisition, running, searching, and reporting—but lacks explicit delete/revoke operations for drafts or entitlements.