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gemmaai-mcp

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Gemma AI MCP Server

Gemma AI - Free Google Gemma Chat with Advanced AI Models

MCP Badge License: MIT Zero Config Stdio Transport MCP

A Model Context Protocol server that exposes the canonical Gemma AI knowledge surface — models, prompts, and chat workflows, pricing, FAQ, official links — to MCP-compatible AI clients such as Claude Desktop, Cursor, Windsurf, and Continue. Read-only, no API keys, no quota, ~50 ms cold start.

Official website: https://gemmaai.online

💬 About Gemma AI

Gemma AI (gemmaai.online) is a web-based platform that provides free access to Google DeepMind's Gemma 4 family of open-source AI models through a conversational chat interface. No credit card or subscription is required to get started. The platform covers four model variants spanning a wide capability range, from compact edge-optimized builds designed for mobile and browser deployment all the way to a flagship 31-billion-parameter dense model that ranks among the top three on the Arena AI public leaderboard. All models are released under the Apache 2.0 license, making them suitable for both personal experimentation and commercial use.

Related MCP server: Muse AI Image MCP Server

Key Features

  • Four-tier model family: Choose from the E2B/E4B ultra-compact edge models (2.3B and 4.5B effective parameters), a 26B Mixture-of-Experts variant that activates only 4B parameters per inference step, or the full 31B dense flagship — each tuned for different resource and performance trade-offs.

  • Native multimodal input: All models accept text, images at variable aspect ratios, video clips, audio, and documents (including OCR and diagram understanding) within a single conversation turn.

  • Extended context windows: Supports 128K to 256K token contexts with dual RoPE configurations, allowing long documents, codebases, and multi-turn sessions without truncation.

  • Strong benchmark performance: The 31B model scores 89.2% on AIME 2026 math reasoning, 80% on LiveCodeBench coding challenges, 85.2% on MMLU Pro, and a 2150 ELO rating on Codeforces — figures visible on the site's benchmark comparison table.

  • Flexible deployment paths: Beyond the hosted chat interface, models can run locally via Ollama, llama.cpp, or MLX; in browsers via transformers.js and WebGPU; and through ONNX checkpoints, Kaggle, or Hugging Face repositories.

  • Function calling without fine-tuning: Built-in support for autonomous agent workflows and tool integration, usable directly from the chat interface or via API.

Use Cases

  • Software development and competitive programming: Developers use the chat interface to generate, debug, and review code, with the 31B model achieving competitive-level performance on standard coding benchmarks.

  • Mathematical and logical reasoning: Students and researchers work through multi-step math problems, proofs, and quantitative analysis tasks that benefit from the model's high AIME and MMLU Pro scores.

  • Document and image analysis: Teams upload PDFs, screenshots, diagrams, or scanned pages to extract structured information, summarize content, or answer questions grounded in visual data.

  • Edge and privacy-sensitive applications: Developers building mobile or browser-based products use the compact E2B/E4B models, which run on-device without sending data to external servers.

  • Rapid prototyping with open-weight models: Researchers and startups evaluate Gemma 4 capabilities through the hosted interface before committing to local or cloud deployment under the permissive Apache 2.0 license.

Who Is It For

Gemma AI serves a broad technical audience. Developers and researchers who want access to frontier open-source models without a subscription barrier will find the free chat interface immediately useful. Teams evaluating models for commercial products benefit from the Apache 2.0 licensing and the range of deployment options. Students working on math, coding, or multimodal projects get access to high-performing models that would otherwise require cloud API budgets. Developers building privacy-first or offline-capable applications can use the edge-optimized variants as a starting point before moving to local deployment with Ollama, llama.cpp, or browser-native runtimes.

Tools

list_models

Return the canonical list of chat models exposed on the site, with capability notes. (Gemma AI)

Input: no parameters. Returns: text/markdown.

get_pricing

Return the canonical pricing entry point for Gemma AI.

Input: no parameters. Returns: text/markdown.

get_official_links

Return the canonical list of official links for Gemma AI (website, support, docs when available).

Input: no parameters. Returns: text/markdown.

Resources

  • site://gemmaai/models — Supported chat models and capability notes.

  • site://gemmaai/pricing — Canonical pricing entry point.

  • site://gemmaai/faq — Short FAQ generated from public site metadata.

  • site://gemmaai/links — Canonical URLs to share with users.

Prompts

tell_me_about_gemmaai

Summarize what the site is, who it's for, and how it works. — Gemma AI

start_chat_session_gemmaai

Open a chat-evaluation session against the site's models, with sensible defaults. — Gemma AI

Installation

Install via Smithery

npx -y @smithery/cli install gemmaai-mcp --client claude

(Replace claude with cursor, windsurf, or continue for those clients.)

Install from source

git clone https://github.com/rocnubie/gemmaai-mcp.git
cd gemmaai-mcp
pnpm install

Then add to your MCP client config (claude_desktop_config.json for Claude Desktop, mcp.json for Cursor / Windsurf / Continue):

{
  "mcpServers": {
    "gemmaai-mcp": {
      "command": "node",
      "args": [
        "/absolute/path/to/gemmaai-mcp/src/index.mjs"
      ]
    }
  }
}

Debug with MCP Inspector

npx @modelcontextprotocol/inspector node src/index.mjs

Official Links

Development

pnpm install
pnpm start                 # run the server over stdio

License

MIT

Available Tools

3 tools
get_pricingC

Return the canonical pricing entry point for Gemma AI.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

C2.8/5.0
Behavior1/5

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

No annotations are provided, so the description must fully convey behavioral traits. It only says 'Return,' implying a read operation, but lacks details on what is returned, side effects, authorization needs, or rate limits. This is insufficient transparency.

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 short sentence with no wasted words. It is appropriately concise for a tool with no parameters. However, it could be slightly expanded to add value without losing conciseness.

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?

With no output schema and no annotations, the description should explain what the return value contains. 'Pricing entry point' is ambiguous. The tool is simple but incomplete in informing the agent about expected output.

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?

There are zero parameters, so the baseline score is 4. The description adds no parameter information, but none is needed since the schema already covers 100% of parameters (none exist).

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 states 'Return the canonical pricing entry point for Gemma AI.' It clearly identifies the verb (Return) and resource (pricing entry point). While it distinguishes from siblings list_models and get_official_links, the term 'entry point' is slightly vague and could be more specific.

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. Sibling tools are listed but no exclusion criteria or context is given. The description does not mention any prerequisites or conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_modelsA

Return the canonical list of chat models exposed on the site, with capability notes. (Gemma AI)

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description must convey behavioral traits. It indicates a read-only operation ('Return... list') with no side effects. Although it does not explicitly state non-destructiveness or auth needs, the simplicity of listing models makes the behavior clear.

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 a single, well-structured sentence that front-loads the action and resource. No unnecessary words or repetition.

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 no parameters and no output schema, the description is mostly complete. It mentions 'with capability notes', hinting at output content. However, it could be improved by specifying the output format (e.g., 'returns an array of model objects').

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 baseline is 4. The description does not need to add parameter information. It effectively communicates the tool's purpose without requiring parameter details.

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 verb 'Return' and the resource 'canonical list of chat models' with additional details about capability notes. It effectively distinguishes from siblings 'get_pricing' and 'get_official_links' which cover pricing and links, not models.

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 retrieving chat models but does not provide explicit guidance on when to use this tool versus alternatives like 'get_pricing' or 'get_official_links'. No when-not or exclusion criteria are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv0.1.0
    • First observedget_official_links
    • First observedget_pricing
    • First observedlist_models
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TDQS

A3.8/5.0
Disambiguation5/5

Each tool has a distinctly different purpose: listing models, getting pricing, and getting official links. There is no ambiguity in their roles.

Naming Consistency5/5

All three tools follow the consistent verb_noun pattern (list_models, get_pricing, get_official_links), making them predictable and easy to understand.

Tool Count5/5

With 3 tools, the set is well-scoped for providing essential information about Gemma AI (models, pricing, links). It is neither too thin nor excessive.

Completeness4/5

The tool surface covers the key informational needs for Gemma AI. A minor gap is the lack of a tool for detailed per-model capabilities beyond the listing, but overall it is reasonably complete.

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