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Llama AI MCP Server

by rocnubie
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Llama AI MCP Server

Llama AI Chat | Llama 4 Maverick for Code and Documents

MCP Badge License: MIT Node Read Only MCP Zero Config

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.

Related MCP server: APIAny MCP

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

Available Tools

3 tools
get_pricingA

Return the canonical pricing entry point for Llama AI.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.8/5.0
Behavior2/5

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

No annotations provided. Description does not disclose what exactly the 'pricing entry point' contains (e.g., URL, structured data), leaving behavior partially opaque.

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?

Single sentence, no redundant information, perfectly concise for the tool's complexity.

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 zero parameters and no output schema, description is adequate but could specify return format for full completeness.

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?

No parameters in schema; description adds context that it returns Llama AI pricing, meeting baseline for a zero-param tool.

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?

Description uses specific verb 'Return' and resource 'canonical pricing entry point', clearly distinguishing from sibling tools 'list_models' and 'get_official_links'.

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?

Implied usage for pricing info, but no explicit when-to-use or when-not-to-use guidance relative to siblings.

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. (Llama AI)

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden for behavioral disclosure. It clearly indicates a read-only operation (return list) and adds 'with capability notes' for additional context. It does not mention side effects or auth, but given the simplicity, this is adequate.

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, front-loaded sentence with no wasted words. It efficiently conveys the tool's output and additional detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters, no output schema, and a simple purpose, the description is complete. It provides the necessary information about what the tool returns (canonical list with capability notes) and includes contextual branding (Llama AI).

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 input schema has zero parameters, so schema coverage is 100%. The description adds no parameter info, but the baseline for zero-parameter tools is 4, as no additional meaning is needed beyond the schema.

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 resource 'canonical list of chat models' with capability notes. It distinguishes from siblings like get_pricing and get_official_links.

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?

No explicit when-to-use or when-not-to-use guidance is given. The purpose is straightforward, and the agent can infer based on sibling tool names, but the description lacks explicit alternatives or context.

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.

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

A4.2/5.0

Scored across 3 tools

Disambiguation5/5

Each tool serves a distinct purpose: listing models, getting pricing, and retrieving official links. There is no overlap or ambiguity.

Naming Consistency5/5

All tool names follow the verb_noun pattern: list_models, get_pricing, get_official_links. Consistent and predictable.

Tool Count5/5

With 3 tools, the set is well-scoped for an informational server, each tool provides essential functionality without being too sparse or bloated.

Completeness4/5

The tools cover the core needs: models, pricing, and links. A minor gap is the lack of detailed model information or search, but the set is reasonable for a simple info tool.

Resources

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