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OpenGerwin

mcp-google-agent-platform-docs

by OpenGerwin
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

list_models

List all AI models on Google's platform, organized by family for quick reference. Includes Google models (Gemini, Imagen), partners (Claude, Grok), and open models (DeepSeek, Qwen).

Instructions

List all available AI models on Google's platform.

Returns a quick reference of all models organized by family: Google (Gemini, Imagen, Veo, Lyria), Partners (Claude, Grok, Mistral, Llama), and Open Models (DeepSeek, Qwen, Kimi, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • src/mcp_google_agent_platform_docs/server.py:317-376 (handler)
    The list_models tool handler. It is decorated with @mcp.tool() and returns a static Markdown string listing all available AI models organized by family (Google, Partners, Open Models).
    @mcp.tool()
async def list_models() -> str:
    """List all available AI models on Google's platform.

    Returns a quick reference of all models organized by family:
    Google (Gemini, Imagen, Veo, Lyria), Partners (Claude, Grok, Mistral, Llama),
    and Open Models (DeepSeek, Qwen, Kimi, etc.).
    """
    await _ensure_initialized()

    # This is a curated static reference that's useful even without cache
    return """# Available AI Models

## Google Models

### Gemini (Text & Multimodal)
| Model | Key Features |
|---|---|
| **Gemini 3.1 Pro** | Latest flagship, 1M context |
| **Gemini 3 Pro** | High quality, balanced |
| **Gemini 3 Pro (Image)** | Native image generation |
| **Gemini 2.5 Pro** | Previous gen flagship |
| **Gemini 3.1 Flash (Image)** | Fast image generation |
| **Gemini 3 Flash** | Speed-optimized |
| **Gemini 2.5 Flash** | Previous gen fast |
| **Gemini 2.0 Flash** | Legacy fast model |
| **Gemini 3.1 Flash Lite** | Ultra-efficient |
| **Gemini 2.5 Flash Lite** | Previous gen lite |
| **Gemini Embedding 2** | Text + code embeddings |

### Imagen (Image Generation)
- Imagen 4.0, Imagen 3.0
- Virtual Try-On, Upscale

### Veo (Video Generation)
- Veo 3.1, Veo 3.0, Veo 2.0

### Lyria (Music Generation)
- Lyria 3, Lyria 002

## Partner Models
| Partner | Models |
|---|---|
| **Anthropic** | Claude Opus 4.7, Sonnet 4.6, Opus 4.5, Haiku 4.5 |
| **xAI** | Grok 4.1 Fast, Grok 4-20 |
| **Mistral** | Mistral Medium 3, Small 3.1, OCR, Codestral 2 |
| **Meta** | Llama 4 Maverick, Llama 4 Scout, Llama 3.3 |

## Open Models (Model-as-a-Service)
| Provider | Models |
|---|---|
| **DeepSeek** | V3.2, V3.1, R1-0528, OCR |
| **Qwen** | Qwen3 Next Instruct/Thinking, Coder, 235B |
| **Kimi** | K2 Thinking |
| **MiniMax** | M2 |
| **OpenAI (open)** | GPT-OSS 120B, 20B |
| **Google** | Gemma 4 26B |

💡 Use `get_doc("models/gemini/3-1-pro")` for detailed model documentation.
"""
  • src/mcp_google_agent_platform_docs/server.py:317-318 (registration)
    Registration via @mcp.tool() decorator on the list_models async function.
    @mcp.tool()
async def list_models() -> str:

Tool Definition Quality

A4/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the burden. It describes the output as a 'quick reference' organized by family, but does not disclose potential behavioral aspects such as authentication requirements, rate limits, or whether it is read-only.

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?

Two sentences, front-loaded with the core purpose, and zero wasted words. Highly efficient.

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 no parameters and an existing output schema, the description is fairly complete: it specifies what is listed and how it is organized. It could mention authentication or that it is a simple read operation, but overall adequate.

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 zero parameters, so schema coverage is effectively 100%. The description does not add parameter info, but none is needed. Baseline for 0 parameters is 4.

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 all available AI models on Google's platform, organized by family. This specific verb+resource combination distinguishes it from sibling tools that deal with documents.

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 usage for listing models, but does not explicitly state when to use this tool versus alternatives. There is no exclusion or guidance on prerequisites, though siblings are clearly document-focused.

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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