Fireworks AI MCP. Build complex generative tasks in chat.

Q: Can I generate images using the Fireworks AI MCP?

Yes, you can use the dedicated image tool. Simply provide a text prompt—like 'a neon jungle at night'—and the system returns a high-fidelity visual asset.

Q: What is the difference between chat and completion?

The chat function is designed for multi-turn conversations, remembering context across several messages. The completion tool is better suited when you just need to finish a single instruction or prompt continuation.

Q: How do I know which models are available before using chat?

You should use the listmodels tool first. This enumerates all active model IDs and versions, letting you pick exactly what you need for your inference.

Fireworks AI gives your agent ultra-fast access to advanced generative models for everything from chat conversations to image creation. It lets you synthesize embeddings, transcribe audio files, or generate text completions instantly, all through one single connection point.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Give Claude and any AI agent real-world access

Run Chat Conversations

Your agent can send chat messages and receive responses from ultra-fast LLMs hosted by Fireworks AI.

Create Vector Embeddings

Generate multi-dimensional vector representations for any array of text strings, making them ready for semantic search or indexing.

Synthesize Images from Text

Command the system to generate high-fidelity images using descriptive text prompts.

Transcribe Audio Files

Pass a public URL for an audio file and receive a flawless, structured textual transcription.

Generate Text Continuations

Complete instructions or prompts by generating basic, high-quality text continuations using state-of-the-art models.

Ask an AI about this

Waiting for input…

AI Agent

What AI agents can do with Fireworks AI with 6 Tools

Use these tools to manage your entire generative workflow—from creating visual assets and transcribing recordings to generating semantic vector data.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Fireworks AI MCP

Embed

Generates vector embeddings for a given set of text strings using Fireworks AI.

List Models

Retrieves an enumerated list of all available high-speed models hosted by Fireworks...

Image

Creates a new, high-fidelity image based on the text description you provide.

Chat

Engages in a multi-turn chat conversation with Fireworks AI's optimized language...

Completion

Generates basic textual completions for continuing an existing prompt or instruction.

Transcribe

Processes a public URL to transcribe the audio content contained within that file.

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Fireworks AI MCP is compatible with Claude

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The Fireworks AI integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "fireworks-ai": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the Fireworks AI tools with full Vinkius guardrails applied.

Fireworks AI MCP is compatible with VS Code

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"fireworks-ai": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on each call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with Fireworks AI, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,200+ others, all in one place
Add new capabilities to your AI anytime you want
Connections are secured and governed automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog weekly

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Fireworks AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

VINKIUS CLOUD

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on each call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Manually handling diverse data inputs is a constant headache.

Think about the process today. You get an audio file, so you copy it into a transcription service and wait for text to populate. Then, you have that text and need to summarize it in Notion, which requires another copy-paste cycle. If you suddenly realize you also needed vector embeddings of that transcript for your search index, you're staring at yet another dashboard and API key.

With this MCP, the flow changes completely. You hand the audio file over to your agent, and it handles the transcription using `transcribe`. Once that text is ready, you can immediately ask it to summarize the action items *and* simultaneously use the generated text to run `embed` for indexing—all in one conversation.

Generate Media & Embeddings with Fireworks AI

The biggest manual time sink is the handoff between media types. You generate an image using a separate service, then you copy that image description into your chatbot to get metadata, and finally, you have to feed all those strings back into a vector store's dedicated API.

Now, you can ask your agent to do it all in one go. Prompt for the visual asset using `image`, and immediately follow up with a request to run `embed` on the prompt description itself. The whole pipeline happens inside your chat window.

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llm-inference

generative-ai

embeddings

model-deployment

high-performance-api

ai-orchestration

What Fireworks AI MCP does for your AI

This MCP connects your favorite AI client directly to Fireworks AI’s high-speed model infrastructure. You get full control over running generative inference without needing complex setups. Need to build a semantic search tool? Use the embeddings synthesis capability. Want to create marketing visuals on the fly? Generate them from text prompts.

The connection also lets you transcribe audio files or run chat completions against optimized LLMs.

It’s designed for developers who need speed and reliability in their AI workflows, letting your agent talk to multiple specialized services through one place. This simplifies integration dramatically; instead of managing several separate API keys, you connect once via Vinkius and get access to all these high-performance tools.

Built · Hosted · Managed by Vinkius Fireworks AI MCP - Generate Images, Embeddings & Transcripts

Server ID 019d759a-23db-713a-b7ee-fa212fbba5a9

Vinkius Inspector

Compliance Grade A+

Score 100/100

Report View Report ↗

How to set up Fireworks AI MCP

The bottom line is that you get fast access to multiple specialized generative AI services through your existing chat interface.

Subscribe to this MCP and input your Fireworks AI API Key into the Vinkius catalog.

Your AI client detects the available tools, allowing you to call functions like embed or image using natural conversation.

The system sends the request to the Fireworks backend, returning the generated data—be it a vector array or a transcribed text string.

Who uses Fireworks AI MCP

Engineers and data scientists who hate slow, complex API calls need this. If your workflow involves turning text into searchable vectors or generating media assets on demand, this is for you.

AI Developer

You use it to test and debug LLM prompts and inference parameters against real-world models without writing boilerplate API integration code.

Data Scientist

You quickly generate embeddings for document sets, then run list_models to ensure you're using the most efficient model for your RAG pipeline.

Product Manager

You test generative features in natural language conversation to validate if the AI can handle edge cases before handing off code to engineers.

Benefits of connecting Fireworks AI MCP

Generate searchable vectors with embed. You can feed it a list of sentences and get back the multi-dimensional arrays needed for semantic search, skipping manual vector library calls.

Need visuals? Use the image tool to create high-fidelity pictures directly from text prompts. It's perfect for rapidly prototyping assets when you don't have design time.

The transcribe function lets your agent pull structured text out of any audio file by passing just a public URL, making media processing simple.

chat handles the heavy lifting of conversation orchestration against ultra-fast LLMs. Your agent keeps track of context across multiple turns without you having to manage session state.

Before building anything, use list_models. This tool lets you check what high-speed models are available and get their specific IDs so your project stays up-to-date.

Fireworks AI MCP use cases

01 01

Processing a Meeting Recording

A product manager uploads an audio recording from a client meeting. They ask their agent to transcribe it using transcribe. The resulting text is then passed back into the chat tool, asking the agent to summarize action items and identify key pain points.

02 02

Building a Document Index

A data scientist has thousands of product manuals. Instead of writing complex code for every document, they ask their agent to run embed on chunks of text from the manuals. This instantly provides the vector arrays needed to index the knowledge base.

03 03

Creating Marketing Content

A marketing team needs a hero image for an upcoming campaign. They prompt their agent, 'Generate a cyberpunk city at sunset.' The image tool runs the inference and returns the visual asset immediately for review.

04 04

Debugging LLM Prompts

An AI developer wants to see how different models handle complex instructions. They use the chat tool, cycling through multiple model IDs retrieved via list_models, to compare outputs quickly and debug their prompt logic.

Fireworks AI MCP tradeoffs

What to watch out for, and the recommended way to handle each one.

Assuming Model Availability

Avoid

A developer tries to run a chat function using an old or unverified model name, resulting in an 'Model Not Found' error and stalling development.

Instead

Always start by running list_models. This guarantees you have the current list of available IDs for high-speed inference, making your code resilient to updates.

Overcomplicating Content Creation

Avoid

A user tries to manually stitch together embedding generation, image creation, and text completion using three different API clients.

Instead

Use this MCP. You can manage all these tasks—embeddings via embed, images via image, and chat completions via chat—all from one natural conversation flow.

Ignoring Input Validation

Avoid

Sending an audio URL to the agent without checking if it's publicly accessible, causing the transcription tool to fail immediately.

Instead

Before calling transcribe, verify the public accessibility of your source material. The tool requires a public URL to function correctly.

When to use Fireworks AI MCP

Use this MCP if your core task involves combining multiple types of generative AI operations in one pipeline: text conversation, media creation, and data vectorization. You need fast inference that can handle everything from chat sessions to image synthesis (image) without switching tools or APIs.

Don't use it if you only need a single function, like simple keyword lookups in a database (use a dedicated database connector) or if your task is purely offline data processing. If you just need to generate text completions for basic forms, the completion tool works well, but if you need semantic search on that content, you'll also want embed. This MCP shines when you have multi-step workflows spanning different media types.

Frequently asked questions about Fireworks AI MCP

How fast is the model inference when I use Fireworks AI MCP? +

The core benefit of this MCP is speed. It connects you to ultra-fast LLMs, meaning complex tasks like chat completions or text generation happen much quicker than with standard API connections.

Can I generate images using the Fireworks AI MCP? +

Yes, you can use the dedicated image tool. Simply provide a text prompt—like 'a neon jungle at night'—and the system returns a high-fidelity visual asset.

What is the difference between `chat` and `completion`? +

The chat function is designed for multi-turn conversations, remembering context across several messages. The completion tool is better suited when you just need to finish a single instruction or prompt continuation.

Do I need special setup for audio transcription with Fireworks AI MCP? +

No. You only need to provide the public URL of the audio file when calling transcribe. The tool handles the processing and returns clean, structured text.

How do I know which models are available before using chat? +

You should use the list_models tool first. This enumerates all active model IDs and versions, letting you pick exactly what you need for your inference.

Give Claude and any AI agent real-world access

What AI agents can do with Fireworks AI with 6 Tools

Embed

Generates vector embeddings for a given set of text strings using Fireworks AI.

List Models

Retrieves an enumerated list of all available high-speed models hosted by Fireworks...

Image

Creates a new, high-fidelity image based on the text description you provide.

Chat

Engages in a multi-turn chat conversation with Fireworks AI's optimized language...

Completion

Generates basic textual completions for continuing an existing prompt or instruction.

Transcribe

Processes a public URL to transcribe the audio content contained within that file.

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Manually handling diverse data inputs is a constant headache.

Generate Media & Embeddings with Fireworks AI

llm-inference

generative-ai

embeddings

model-deployment

high-performance-api

ai-orchestration

What Fireworks AI MCP does for your AI

How to set up Fireworks AI MCP

Who uses Fireworks AI MCP

Benefits of connecting Fireworks AI MCP

Fireworks AI MCP use cases

Processing a Meeting Recording

Building a Document Index

Creating Marketing Content

Debugging LLM Prompts

Fireworks AI MCP tradeoffs

Assuming Model Availability

Overcomplicating Content Creation

Ignoring Input Validation

When to use Fireworks AI MCP

Frequently asked questions about Fireworks AI MCP