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interview-prep-mcp

interview-prep-mcp

by shenmali
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_models_tool

List available AI models from Gemini and OpenRouter with filters for context size, JSON-only, free-only, and refresh options.

Instructions

List available models. Provider 'all' merges Gemini + OpenRouter (top 20).

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_models_tool"
  }
}

Input Schema

NameRequiredDescriptionDefault
providerNoall
min_contextNo
supports_json_onlyNo
free_onlyNo
refreshNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must reveal behavioral traits. It only mentions that provider 'all' merges top 20 models, but omits whether the operation is read-only, cached, or has rate limits. This is insufficient for a tool with no annotations.

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

Conciseness4/5

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

The description is a single, efficient sentence with no wasted words. However, it could be expanded to cover more parameters without becoming verbose. Front-loading is good.

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

Completeness2/5

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

Given 5 parameters and no annotations, the description is too brief. It explains only one parameter's behavior and lacks details about filters (min_context, free_only) and refresh. The output schema may cover return format, but the parameters remain under-documented.

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

Parameters2/5

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

Schema coverage is 0%, so the description should explain all parameters. It only adds meaning for 'provider' (merging behavior), but ignores min_context, supports_json_only, free_only, and refresh. Thus, only 1 of 5 parameters is clarified.

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

Purpose5/5

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

The description clearly states the tool lists available models, which is a specific verb+resource. It also distinguishes from sibling tools like set_model_tool and get_active_model_tool by focusing on listing rather than setting or retrieving.

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 guidance is provided on when to use this tool versus alternatives like set_model_tool or analyze_weak_areas_tool. The description lacks context on when listing models is appropriate.

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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