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Semicolon-D

AI Model Advisor MCP Server

by Semicolon-D

list_models

List and filter available AI models from multiple providers by category, capability, provider, or max price to select the right model for your needs.

Instructions

List and filter available AI models from all 5 providers. Filter by category (llm, text-to-image, text-to-video, text-to-speech, speech-to-text, image-to-3d, etc.), provider (openrouter, fal, together, replicate, fireworks), capability, or max price.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default: 25, max: 100)
categoryNoFilter by category: llm, text-to-image, image-to-image, text-to-video, image-to-video, video-to-video, text-to-speech, speech-to-text, text-to-audio, image-to-3d, vision, embedding
providerNoFilter by provider: openrouter, fal, together, replicate, fireworks
max_priceNoMaximum price in USD. For LLMs: price per 1M tokens (e.g. 1.0 = $1/1M tokens). For media: price per unit (image, second, etc.). Use 0 for free models only.
capabilityNoFilter by capability: "tool_use", "reasoning", "vision", "photorealistic", "fast", etc.
Behavior2/5

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

With no annotations, the description must disclose behavioral traits, but it only says 'List and filter available AI models' without mentioning pagination, ordering, default limits, or return format. The schema covers the limit parameter, but the description adds no context about how results are presented or whether any side effects exist.

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 sentence that front-loads the core purpose and packs in filter examples compactly. It avoids fluff, but the parenthetical enumeration is slightly dense and partially duplicates schema content, keeping it from a perfect score.

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 no output schema and no annotations, the description should explain return values or behavior, but it does not. It omits any mention of what model information is returned (e.g., model IDs, pricing), which is essential for an agent choosing whether to use this tool over sibling tools.

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 description coverage is 100%, so baseline is 3. The description repeats filter names (category, provider, capability, max price) but adds no new meaning beyond the schema's detailed parameter descriptions. It does reinforce that filters exist, but without extra semantic value.

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 'List and filter available AI models from all 5 providers', using a specific verb and resource. It distinguishes itself from siblings like get_model_info (single model) and compare_models (comparison) by emphasizing broad listing and filtering across providers.

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?

Usage is implied through 'List and filter' and the explicit filter categories, but there is no mention of when to prefer this tool over siblings like recommend_model or select_model_for_project. No exclusions or alternative tools are referenced, so guidance is only implicit.

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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