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LuciferForge

agent-safety-mcp

by LuciferForge

cost_guard_models

Lists supported AI models with per-token pricing to help manage API costs and enforce budget controls.

Instructions

List all supported models with their per-token pricing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler implementation for the 'cost_guard_models' tool, which iterates through the PROVIDERS dictionary to return supported models and their pricing.
    def cost_guard_models() -> dict:
        """List all supported models with their per-token pricing."""
        models = {}
        for name, pricing in PROVIDERS.items():
            models[name] = {
                "input_per_1M": round(pricing["input"] * 1_000_000, 2),
                "output_per_1M": round(pricing["output"] * 1_000_000, 2),
            }
        return models
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description carries the full burden. It partially compensates by specifying the return content ('per-token pricing'), indicating the data structure includes cost metrics. However, it lacks critical behavioral context: no indication of side effects (though 'List' implies read-only), rate limits, caching behavior, or whether the data is real-time versus static.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, zero waste. Front-loaded with the verb and core object. No redundant phrases or unnecessary verbosity given the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters and no output schema, the description adequately compensates by specifying what information is returned (model list with pricing). For a simple discovery tool, this is sufficient context, though explicit mention of return format (array, object) would improve it further.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema contains zero parameters. Per scoring rules, zero-parameter tools receive a baseline score of 4. The description appropriately does not invent parameters where none exist.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List') and resource ('supported models'), including specific scope ('per-token pricing'). It effectively distinguishes from siblings like cost_guard_check (validation) and cost_guard_configure (settings management) by focusing on discovery/retrieval of pricing data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives (e.g., when to query this vs. cost_guard_check). No mention of prerequisites or typical workflow position. The description only states what the tool does, not when to invoke it.

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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