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
- Website: https://llamaai.online
- Pricing: https://llamaai.online/pricing
- Support: support@llamaai.online
Development
pnpm install
pnpm start # run the server over stdio
License
MIT
Server Config
{
"mcpServers": {
"llamaai-mcp": {
"command": "node",
"args": [
"/absolute/path/to/llamaai-mcp/src/index.mjs"
]
}
}
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