predictive_access_tiers_info
Explain visibility tiers for AlpineLead predictive layers. No credits or AI calls.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Explain visibility tiers for AlpineLead predictive layers. No credits or AI calls.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It does disclose a meaningful trait: 'No credits or AI calls', which tells the agent this is a low-cost explanatory call. However, it does not explicitly state read-only behavior, potential auth requirements, or what kind of response to expect, so transparency is only partial.
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 short sentences. The primary purpose is front-loaded and the second sentence adds a useful behavioral note without padding. Every word earns its place.
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?
Given the tool's low complexity (no parameters, no nested objects), the description is mostly sufficient: it states what the tool does and that it costs no credits or AI calls. The main gap is the lack of explicit routing or expected output information, but the simplicity of the tool keeps this from being a serious deficiency.
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 tool has zero parameters and the schema has no properties, so there is no parameter documentation burden. The description does not need to add parameter-level meaning. The baseline for 0-parameter tools is 4, and nothing in the description detracts from that.
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 action ('Explain') and the target resource ('visibility tiers for AlpineLead predictive layers'), making the tool's purpose identifiable. It is reasonably distinct from sibling predictive_*_info tools because it focuses specifically on access/visibility tiers rather than a model behavior. It stops short of a 5 because it does not explicitly contrast itself with those sibling tools.
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?
There is no guidance about when to choose this tool over the many sibling info tools, nor any mention of prerequisites such as authentication. The phrase 'No credits or AI calls' is a cost/behavior note, not a usage guideline. The agent is left to infer that this tool should be used when explaining access tiers.
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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