Get Agent AVCP report
get_ai_pack_reportRead the buyer-safe, version-bound Agent AVCP gates and scores for a published pack.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| productId | Yes | Tetrees AI Pack product id |
get_ai_pack_reportRead the buyer-safe, version-bound Agent AVCP gates and scores for a published pack.
| 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?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is established. The description adds useful context about the report being buyer-safe and version-bound, but it does not disclose behaviors such as error conditions, version lookup behavior, or what happens when the pack is not published. This is adequate but not rich.
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, front-loaded sentence with no filler. Every phrase adds meaning: 'Read' signals the operation, 'buyer-safe' and 'version-bound' scope the behavior, and 'published pack' clarifies applicability.
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 read-only tool with no output schema, the description is nearly complete. It identifies what the report contains (AVCP gates and scores), who it is for (buyer-safe), and the lifecycle state (published). It could be more explicit about the response shape, but the content is sufficiently implied.
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 schema covers 100% of the single parameter, productId, with a format and description. The description does not add further parameter-level meaning beyond noting the published, version-bound context, which aligns with the schema's baseline adequacy. No compensation is needed because schema coverage is complete.
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 begins with the specific verb 'Read' and clearly identifies the resource: buyer-safe, version-bound Agent AVCP gates and scores for a published pack. It also distinguishes itself from the sibling get_ai_pack_private_report by emphasizing 'buyer-safe' and 'published,' making the tool's purpose unambiguous.
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 description provides clear context by specifying 'published pack' and 'buyer-safe,' which implies this is the appropriate tool for public-facing, version-bound AVCP reports rather than private or draft state reports. It does not explicitly name an alternative tool or state exclusions, but the context is strong enough given the sibling set.
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.