quote_agent_runGet the exact worst-case Point reservation before using platform-funded inference. BYOK quotes zero model points.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| productId | Yes | Tetrees AI Pack product id | |
| fundingMode | No | points | |
| enabledSkills | No | ||
| growthVersion | No | Hosted intelligence checkpoint. Omit to use the account default; 0 runs the signed base Pack. | |
| maxInputTokens | No | ||
| maxOutputTokens | No |
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 indicate read-only and non-destructive behavior. The description adds the behavioral nuance that BYOK quotes zero model points, but doesn't go beyond that. It doesn't contradict annotations.
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 extremely concise, two sentences with no filler. It delivers the core info efficiently, making it easy to parse.
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 simple quote tool, the description covers the main purpose and a key nuance. It doesn't mention return format or prerequisites, but these are not critical given the tool's simplicity and the absence of an output schema.
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 description coverage is low (29%), and the tool description does not compensate by explaining any parameters. It omits details about fundingMode, enabledSkills, token limits, etc., which are critical for correctly using the tool.
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 states the tool's purpose: to get the exact worst-case Point reservation before using platform-funded inference. It also notes the BYOK zero-point nuance, which helps differentiate from similar quote tools like quote_ai_pack_audition by focusing on runs.
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?
It specifies when to use the tool (before platform-funded inference) and provides a condition for BYOK quoting zero points. However, it doesn't explicitly compare with alternatives like quote_ai_pack_audition, though the run-specific context is implied.
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.