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Cloudflare Workers AI MCP Server

Cloudflare Workers AI MCP Server

by puspoaditya
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Cloudflare Workers AI MCP Server

Model Context Protocol (MCP) server that gives AI agents access to Cloudflare Workers AI — serverless LLM inference, embeddings, and image generation with a generous free tier.

Tools

Tool

Description

list_models

List supported chat, embedding, and image models

chat_completion

LLM chat completion (Llama 3.3 70B, Llama 3.1 8B, Llama 4 Scout, Qwen Coder 32B, DeepSeek R1 Distill)

embed_text

Text embeddings (BGE small/base)

generate_image

Image generation (Flux 1 Schnell) → base64 image (PNG/JPEG)

Related MCP server: Foundry Agents MCP Server

Setup

  1. Create a Cloudflare API token with the Workers AI permission: https://dash.cloudflare.com/profile/api-tokens

  2. Get your Account ID (right sidebar of the Cloudflare dashboard, or the /accounts/{id} segment of any dashboard URL)

  3. Export the env vars:

export CLOUDFLARE_ACCOUNT_ID="your-account-id"
export CLOUDFLARE_API_TOKEN="your-api-token"

Run

npm install && npm run build
npm start   # stdio MCP server

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "cloudflare-workers-ai": {
      "command": "node",
      "args": ["/absolute/path/to/cloudflare-workers-ai-mcp/dist/index.js"],
      "env": {
        "CLOUDFLARE_ACCOUNT_ID": "your-account-id",
        "CLOUDFLARE_API_TOKEN": "your-api-token"
      }
    }
  }
}

Cursor / VS Code / other MCP clients

Point the client at the same command (node dist/index.js with the two env vars).

OpenClaw

OpenClaw supports MCP servers — add the same command to its MCP configuration.

Example prompts

  • "Summarize this text using the Llama 3.3 70B model on Cloudflare Workers AI"

  • "Generate an image of a red fox in a snowstorm"

  • "Embed these 3 sentences for a similarity search"

Models (verified live)

Chat: POST /ai/v1/chat/completions (OpenAI-compatible) · Embeddings & images: POST /ai/run/{model} (native)

Models — chat: @cf/meta/llama-3.3-70b-instruct-fp8-fast · @cf/meta/infire-llama-3.1-8b-instruct · @cf/meta/llama-4-scout-17b-16e-instruct · @cf/qwen/qwen2.5-coder-32b-instruct · @cf/deepseek-ai/deepseek-r1-distill-qwen-32b

Embeddings: @cf/baai/bge-small-en-v1.5 · @cf/baai/bge-base-en-v1.5

Images: @cf/black-forest-labs/flux-1-schnell

Note: @cf/meta/llama-3.1-8b-instruct was deprecated by Cloudflare (2026-05-30) — use @cf/meta/infire-llama-3.1-8b-instruct.

Test

npm test   # unit tests (mocked fetch) + protocol test

License

MIT — built by puspoaditya.

Available Tools

4 tools
chat_completionA

Run an LLM chat completion using Cloudflare Workers AI. Use this tool when you need direct LLM inference with an explicit message list (system/user/assistant roles) — for example summarization, translation, Q&A, classification, or code generation. The request is sent to the Workers AI OpenAI-compatible endpoint and the full assistant reply is returned as a single text block together with the model id and finish reason. No streaming is performed. Requires CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN environment variables; costs are billed to your Cloudflare account (free tier available).

Inputs: model (required), messages (required), max_tokens, temperature.

embed_textA

Generate numerical vector embeddings for one or more text inputs using Cloudflare Workers AI. Use this tool when you need semantic vector representations of text — for example similarity search, clustering, deduplication, or retrieval-augmented generation. Returns one embedding array (384-dimensional for BGE small, 768 for BGE base) per input text, in the same order as the inputs. Embeddings are generated by the native Workers AI endpoint and are deterministic for identical inputs. Requires CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN environment variables.

Inputs: model (required), texts (required).

generate_imageA

Generate an image from a text prompt using Cloudflare Workers AI (Flux 1 Schnell). Use this tool when the user asks for a picture, illustration, concept art, or visual asset — e.g. logos, scene descriptions, thumbnails, or design mockups. The image is generated by the Flux 1 Schnell model on Workers AI and returned as a base64-encoded string; decode it to obtain the image file (PNG or JPEG depending on the model output). Generation is asynchronous and typically takes a few seconds. Note: Cloudflare applies content moderation — prompts containing NSFW content are rejected with an error. Requires CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN environment variables.

Inputs: prompt (required), steps.

list_modelsA

List the Cloudflare Workers AI models supported by this server. Use this tool to discover available model ids before calling chat_completion, embed_text, or generate_image — it returns the complete catalog of chat models, embedding models, and image models with their roles. This tool does not call the Cloudflare API and works without credentials.

Inputs: no named parameters.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.7/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool addresses a distinct capability: discovery (list_models), text generation (chat_completion), vector embeddings (embed_text), and image generation (generate_image). There is no overlap in purpose, making selection unambiguous.

    Naming Consistency5/5

    All tool names follow a clear verb_noun pattern (list_models, chat_completion, embed_text, generate_image) with consistent snake_case. The style is uniform and predictable, aiding agent understanding.

    Tool Count5/5

    With only four tools, the server is tightly scoped to core AI inference tasks (listing, chat, embeddings, images). Each tool is essential and the count is well below the threshold for bloat, making the surface easy to navigate.

    Completeness4/5

    The server covers the three primary inference modalities advertised (chat, embeddings, images) plus model discovery. Minor gaps exist (e.g., audio or translation tasks), but for its stated purpose as a Workers AI inference wrapper, the surface is reasonably complete.

    Maintenance

    ActivityInactive
    ResponsivenessOutdated data

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