Llama AI MCP Server

@rocnubie

13 days ago

Overview

Llama AI MCP Server

Llama AI Chat | Llama 4 Maverick for Code and Documents

A Model Context Protocol server that exposes the canonical Llama 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://llamaai.online

💬 About Llama AI

Llama AI (llamaai.online) is a browser-based chat workspace built around Meta's Llama 4 family of models, with Llama 4 Maverick available by default. The site is designed as an independent evaluation environment — not an official Meta product — that lets individuals and teams run real workloads against the model without setting up local infrastructure or configuring an API. Conversations can include plain text, uploaded files, and images, making it practical for a wide range of technical and research tasks. A pricing page and model comparison pages (covering alternatives such as DeepSeek and Qwen) help users make informed decisions before committing to deeper integration.

Key Features

Live model selection — switch between available Llama 4 variants from within the chat interface without any additional setup.
Multimodal input — upload images, screenshots, diagrams, PDFs, and document files alongside text prompts in a single conversation thread.
Long-document handling — synthesize extended PDFs, decision memos, and notes; the model surfaces risks and contradictions across large inputs.
Code-focused workflows — paste repository diffs, stack traces, or code snippets and receive actionable review comments or bug triage.
Export for team handoff — save conversation outputs as shareable artifacts for review by other team members.
Localization — the interface supports English, German, French, Japanese, Korean, Spanish, Arabic, Dutch, and Turkish.
Model comparison pages — side-by-side capability comparisons against other frontier models help contextualize Llama 4's strengths and trade-offs.

Use Cases

Code review and refactoring — submit a pull request diff or a failing test output and get structured feedback on logic errors, security issues, or suggested rewrites.
Document analysis — load lengthy research papers, legal documents, or internal memos and ask the model to extract key points, flag contradictions, or draft summaries.
Visual context interpretation — upload UI screenshots or architecture diagrams and ask questions about layout decisions, data flows, or interface problems.
Research synthesis — compare findings across multiple documents in one thread, useful for literature reviews or competitive analysis.
Pre-integration evaluation — run representative production workloads through the model before investing in API credentials, hosted infrastructure, or custom fine-tuning pipelines.

Who Is It For

Llama AI is built primarily for software engineers, technical leads, and research teams who want to assess whether Meta's Llama 4 models fit their use case before making infrastructure or budget commitments. The browser-first design removes the friction of local model deployment, making it accessible to people who want results quickly rather than spending time on environment configuration. It is also useful for product managers and analysts who need to work with large documents or mixed text-and-image inputs and prefer a straightforward chat interface over raw API calls. The explicit model comparison pages suggest the site is also aimed at teams actively evaluating multiple open-weight models in parallel.

Tools

`list_models`

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

Input: no parameters. Returns: text/markdown.

`get_pricing`

Return the canonical pricing entry point for Llama AI.

Input: no parameters. Returns: text/markdown.

`get_official_links`

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

Input: no parameters. Returns: text/markdown.

Resources

site://llamaai/models — Supported chat models and capability notes.
site://llamaai/pricing — Canonical pricing entry point.
site://llamaai/faq — Short FAQ generated from public site metadata.
site://llamaai/links — Canonical URLs to share with users.

Prompts

`tell_me_about_llamaai`

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

`start_chat_session_llamaai`

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

Installation

Install via Smithery

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

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

Install from source

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

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

{
  "mcpServers": {
    "llamaai-mcp": {
      "command": "node",
      "args": [
        "/absolute/path/to/llamaai-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

Try in Playground

Server Config

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

Build with ShipAny.