soif-mcp

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list_known_models

List AI models recognized by SOIF, including size tier and default hosting. Review how unknown models fall back to the large tier.

Instructions

List models soif recognises, with their size tier and default hosting.

Unknown models still work everywhere (they fall back to the "large" tier with an explicit assumption), but known models get calibrated tier/provider/region defaults.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_known_models"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 21, 2026

TDQS

A3.9/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that this is a read-only operation (listing), that it only includes known models, and importantly explains that unknown models fall back to a default tier with an assumption. This is meaningful behavioral context that helps the agent understand the tool's scope and limitations.

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?

The description is concise and front-loaded: the first sentence states the core function, the second adds essential context about fallback behavior. Every sentence earns its place, with no wasted words or redundant details.

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 the tool has no parameters and an output schema exists (which presumably lists the models), the description is complete in explaining what the tool does and the key context about unknown models. It could potentially mention that the output includes the default hosting or that it's a read-only call, but these are minor gaps; the output schema likely covers return details.

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?

The tool has 0 parameters, and the schema coverage is 100% (there are no properties), so the description does not need to explain parameters. The baseline for 0 params is 4, and the description adds no param info, which is appropriate since there's nothing to explain.

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 tool lists models with size tier and default hosting, which is a specific verb and resource. It does not explicitly distinguish from siblings like compare_models or pick_low_water_model, but the purpose is clear enough that an agent would understand it's a listing operation, not a comparison or selection.

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

Usage Guidelines3/5

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

The description implies when to use this tool (when you need the list of known models with their defaults) but does not provide explicit when-not or alternative suggestions. It does add context that unknown models work everywhere, which helps an agent decide if listing known models is necessary, but it doesn't reference sibling tools for different needs.

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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