Decision models
modelsList the decision models with rates, the pricing formula, limits and the request and answer shapes. Free.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
modelsList the decision models with rates, the pricing formula, limits and the request and answer shapes. Free.
| 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?
Annotations already establish readOnlyHint=true, so the safety profile is covered. The description adds genuinely new behavioral context by disclosing the cost model ("Free") and the exact contents of the response, which matters because there is no output schema to describe return values. It stops short of stating rate limits or whether pricing varies per model.
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?
One sentence carries the payload detail and the front-loaded verb plus resource tells the agent immediately what happens. The one-word "Free." fragment is terse but pays for itself by flagging cost.
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?
With zero parameters and no output schema, the description carries the burden of describing what comes back, and it does so by listing the specific fields returned. The only missing piece is an explicit pointer to the `decide` sibling as the follow-up action.
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 takes zero parameters, so the baseline applies; there is nothing for the description to disambiguate. The description's mention of rates, formula, limits, and request/answer shapes is about output rather than input, leaving parameter semantics trivially satisfied.
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?
Names a specific verb and resource ("List the decision models") and enumerates the payload contents an agent will get back: rates, pricing formula, limits, and request/answer shapes. It does not name the sibling `decide` tool, so the boundary between discovery and execution is only implied by the word "Free".
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 single word "Free" hints that this is the no-cost exploration call before invoking the paid `decide` sibling, which is a useful implicit signal. However, there is no explicit when-to-use or when-not-to-use statement, and the alternative tool is never named.
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.