Check AI Pack submission readiness
get_ai_pack_submission_readinessReturn missing TAIP, metadata, and imagery requirements with exact next actions.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| productId | Yes | Tetrees AI Pack product id |
get_ai_pack_submission_readinessReturn missing TAIP, metadata, and imagery requirements with exact next actions.
| Name | Required | Description | Default |
|---|---|---|---|
| productId | Yes | Tetrees AI Pack product id |
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?
The annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is clear. The description adds behavior beyond the annotations by stating that it returns specific missing items and exact next actions, not just a readiness flag or boolean. It does not address response format, but for a read-only readiness check this is adequate.
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?
One concise sentence, approximately ten words, with the key output categories front-loaded and no filler or repetition of the title. Every word contributes meaning.
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?
With one required parameter, no output schema, and simple read-only annotations, the description sufficiently states what the tool returns and that it provides actionable next steps. A slightly more structured response description or expansion of 'TAIP' would help, but nothing critical prevents an agent from calling and correctly interpreting the tool's purpose.
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?
The only parameter productId is fully described in the schema as 'Tetrees AI Pack product id' with a UUID format, giving 100% schema description coverage. The description adds no parameter-specific details, so the baseline of 3 applies per the rubric.
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 clearly identifies the verb ('Return'), the resource ('missing TAIP, metadata, and imagery requirements'), and the added value ('exact next actions'), distinguishing it from submit/upload sibling tools. The only slight weakness is the unexplained 'TAIP' acronym, which may be unclear to an agent without domain knowledge.
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?
Usage context is implied by the title 'Check AI Pack submission readiness' and the description's focus on missing requirements, suggesting it belongs before submit/update/upload workflows. However, it does not explicitly say when to use it versus the sibling tools, and no alternatives or exclusions are mentioned.
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.