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AI Model Advisor MCP Server

AI Model Advisor MCP Server

by Semicolon-D
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

compare_models

Compare AI models side-by-side across providers and categories. View speed metrics and benchmark scores to select the right model for your requirements.

Instructions

Compare AI models side-by-side in a table. Works across providers and categories — compare LLMs against each other, image generators, or even mix categories. Auto-adapts columns based on model types. Includes speed metrics (TTFT, tokens/sec) and benchmark scores (MMLU) when available.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model_ids": "<model_ids>"
    },
    "name": "compare_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_idsYesModel IDs to compare. Examples: ["openai/gpt-4o", "anthropic/claude-sonnet-4"] or ["fal-ai/flux-pro/v1.1", "fal-ai/stable-diffusion-v35-large"]

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.3.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that the output is a table, columns auto-adapt based on model types, and metrics (TTFT, tokens/sec, MMLU) are included 'when available,' giving a realistic expectation of data completeness. It does not mention error handling or rate limits, but for a read-only comparison tool this is acceptable.

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?

Three sentences, each earning its place: it states the core function, the scope (across providers/categories), and the automatic column adaptation plus included metrics. The description is front-loaded with the main purpose and avoids fluff.

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?

For a tool with one parameter and no output schema, the description is largely complete: it explains what the tool does, the output format, the adaptability behavior, and what metrics are shown. Minor gaps remain around error handling and exact return structure, but the description covers the essential context.

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

Parameters3/5

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

Schema coverage for the single parameter is 100%, with an example list of model IDs. The description does not add parameter-specific details beyond what the schema provides, so the baseline of 3 is appropriate. It does clarify the intent of the parameter indirectly by describing the comparison functionality.

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 states a specific verb ('Compare') and resource ('AI models') with a clear output format ('in a table'). It distinguishes itself from sibling tools like list_models and get_model_info by emphasizing side-by-side comparison across providers and categories.

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

Usage Guidelines4/5

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

The description clearly implies when to use this tool (when needing to compare models side-by-side, including across categories) and mentions auto-adapting columns and available metrics. It does not explicitly name alternatives or exclusions, but the context is evident from sibling tool names.

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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