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Tetrees AI

Inspect AI Pack execution skills

get_ai_pack_runtime_profile
Read-only

Return the Pack-declared hosted skills, ephemeral attachment policy, and user-controlled MCP/API extensions before a run.

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 observed

TDQS

A4.2/5.0
Behavior3/5

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

The readOnlyHint and destructiveHint annotations already cover the safety profile, and the description's 'Return' aligns with read-only behavior. The description adds some context (what is returned) but does not introduce additional behavioral caveats beyond the annotations, so a middle score is appropriate.

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?

The description is a single, well-structured sentence that directly conveys the tool's purpose without redundancy or extraneous detail. It is concise and immediately understandable.

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

Completeness4/5

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

The tool is simple (one parameter, no output schema), and the description explains what is returned. It does not specify the response format, but given the absence of an output schema and the low complexity, the information provided is sufficient for correct usage.

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

Parameters4/5

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

The single parameter (productId) has a clear description ('Tetrees AI Pack product id') and format (uuid), achieving 100% schema coverage. The description adds a small amount of context by naming the product, but it is not needed to understand the parameter's purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (return) and the resource (Pack-declared hosted skills, ephemeral attachment policy, and user-controlled MCP/API extensions) with a specific context ('before a run'), making the tool's purpose unambiguous. While it does not explicitly name sibling tools, the specificity is sufficient to distinguish it from related run/preparation tools.

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 phrase 'before a run' provides clear usage context, implying this tool is for pre-run inspection. However, it does not explicitly state when not to use it or mention alternative tools, so it falls short of full explicit guidance.

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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TDQS

B3.1/5.0
Disambiguation4/5

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.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern, with clear action prefixes and descriptive resource nouns.

Tool Count2/5

With 26 tools, the set exceeds the recommended range and feels heavy, even though the domain covers publishing, runtime, growth, and reporting workflows.

Completeness4/5

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.

Resources