Acquire a free Tetrees AI Pack
acquire_free_ai_packCreate a revocable entitlement for a published free pack without Stripe.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| productId | Yes | Tetrees AI Pack product id |
acquire_free_ai_packCreate a revocable entitlement for a published free pack without Stripe.
| 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?
With no annotations, the description carries the full burden. It discloses the key trait 'revocable' and restricts to 'published free pack', but it does not specify what happens if the pack is not free/published, whether the operation is idempotent, or what the response contains. Some behavioral context is present but not comprehensive.
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?
A single sentence, front-loaded with the core action, no filler words. It conveys the essential purpose and differentiators efficiently.
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 one-parameter tool, the description covers the essential purpose but omits error conditions and return behavior. It lacks detail on conflicts (e.g., existing entitlement) and does not clarify what 'revocable' means in terms of API behavior. Adequate for a simple operation but not fully complete.
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 100%, so the schema already documents productId. The description adds only the implicit constraint that the product must be a published free pack, which is not a parameter-level detail. Baseline 3 is appropriate.
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 states a precise verb ('Create') and resource ('revocable entitlement') and adds qualifiers ('published free pack', 'without Stripe') that sharply distinguish this tool from siblings like accept_ai_pack_terms or publish_ai_pack. It is unambiguous about the tool's core action.
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?
The phrase 'without Stripe' implies this is the alternative path when Stripe is not involved, giving context for when to choose this tool. It does not name a specific sibling, but the condition is clear enough for an agent to infer the intended use case (free, published packs bypassing payment).
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 have clearly distinct roles across the pack lifecycle, but the growth/audition/quote cluster and multiple get_*_report/profile/readiness tools could cause an agent to pause and read descriptions carefully. The descriptions do help separate them, so the ambiguity is minor rather than severe.
All tool names follow a consistent lowercase snake_case verb_noun pattern, with prefixes like list_, get_, run_, create_, update_, and upload_ used uniformly. The different noun segments reflect different resources, not inconsistent naming conventions.
With 26 tools, the surface is slightly heavy, but it maps to a broad set of legitimate operations: drafting, publishing, auditing, running, growth, quoting, model listing, and upload/download workflows. Each tool appears purposeful, and none are obvious duplicates.
The tool set covers most major lifecycle actions, but there are notable gaps such as no delete/unpublish for packs or drafts, no simple get_ai_pack by ID, and no cancel operation for runs or auditions. These missing operations prevent full CRUD/lifecycle coverage.