Fireworks AI MCP. Run chat, embed, image, and transcribe from one API.
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Fireworks AI MCP Server connects your AI agent to high-speed generative services. Use this to perform chat completions, generate embeddings, create images from prompts, transcribe audio, and manage model lists all through one unified API.
It's built for developers needing ultra-fast, reliable LLM inference and multi-modal content generation.
What your AI agents can do
Chat
Sends chat messages to the server and gets a conversational response using Fireworks AI.
Completion
Generates basic text continuations for prompts or instructions using Fireworks AI.
Embed
Creates multi-dimensional vector embeddings from input strings using Fireworks AI.
Your agent sends chat messages and receives immediate, high-speed text completions using the chat tool.
The agent generates basic text continuations for a prompt or instruction using the completion tool.
The agent processes arrays of strings and returns multi-dimensional vector representations for semantic search using the embed tool.
The agent sends a text prompt and receives a high-fidelity image generated by the image tool.
The agent provides a public URL, and the transcribe tool returns the structural text content of the audio file.
The agent uses the list_models tool to enumerate available model IDs and check model capabilities.
Ask AI about this MCP
Supported MCP Clients
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Fireworks AI MCP Server: 6 Tools for Generative AI
These tools give your agent direct access to chat, image generation, embedding, transcription, and more, all powered by Fireworks AI.
019d759achat
Sends chat messages to the server and gets a conversational response using Fireworks AI.
019d759acompletion
Generates basic text continuations for prompts or instructions using Fireworks AI.
019d759aembed
Creates multi-dimensional vector embeddings from input strings using Fireworks AI.
019d759aimage
Generates a high-fidelity image based on a text prompt using Fireworks AI.
019d759alist models
Retrieves a list of available model names and capabilities from Fireworks AI.
019d759atranscribe
Transcribes the structural text content of an audio file provided by a public URL using Fireworks AI.
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 every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Fireworks AI, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Fireworks AI MCP Server connects your AI agent to high-speed generative services. You can use this to chat with the server and get a conversational response using the chat tool. You can generate basic text continuations for a prompt or instruction using the completion tool. You can create multi-dimensional vector embeddings from an array of strings for semantic search using the embed tool.
The server can generate a high-fidelity image when you give it a text prompt via the image tool. You can get the structural text content of an audio file by giving the transcribe tool a public URL. You'll also use the list_models tool to get a list of available model names and capabilities.
How Fireworks AI MCP Works
- 1 Subscribe to the Fireworks AI server and input your API key into your agent client.
- 2 Your agent sends a request (e.g., 'Generate an image of a cyberpunk dog') and invokes the specific tool (e.g.,
image). - 3 The server executes the tool, handles the inference, and returns the result (e.g., the image data or text) back to the agent for use.
The bottom line is, your agent talks to the server, the server runs the tool, and the result gets passed back to your conversation flow.
Who Is Fireworks AI MCP For?
This is for the developer who needs to test and build generative AI features without manually writing API calls for every step. If your workflow involves mixing text chat, image creation, and data indexing, this is your server. It’s built for speed and reliability in a complex data environment.
Tests and debugs LLM prompts and inference parameters without leaving the chat interface or writing boilerplate API code.
Generates embeddings and indexes documents for semantic search directly from the IDE or chat flow.
Evaluates different LLM and image models, running comparative tests through natural language prompts.
Monitors model availability and tests generative AI features using simple, conversational language.
What Changes When You Connect
- The
chattool keeps your conversations running. Instead of making a separate API call for every turn, your agent manages the full chat orchestration against ultra-fast LLMs. - The
embedtool eliminates manual vectorization. You pass an array of strings and get vector representations, ready to index for semantic search, all from a single tool call. - The
imagetool lets you skip the image API. Just give a prompt, and the agent handles the synchronous inference to deliver a high-fidelity visual asset. - The
transcribetool processes audio files automatically. You only need to provide a public URL, and the agent gets the clean, structural text extracted. - The
list_modelstool saves time on setup. You can query the server to list all available model IDs and check which ones are fastest for your current task. - By combining these tools, you eliminate the need to switch between multiple services. Your agent stays in one conversational flow, regardless of whether it's generating text, images, or embeddings.
Real-World Use Cases
Building a knowledge retrieval system
A data scientist needs to index 10,000 documents for RAG. Instead of writing a batch script to call a separate embedding service, the agent uses the embed tool, passing the document chunk array. It instantly gets the vectors needed for the vector database, keeping the entire process conversational.
Automating content creation from media
A marketing team wants to create a social media campaign. They first use the transcribe tool on a video meeting recording. Then, the agent uses chat to summarize the transcript and generate five key talking points. Finally, it uses the image tool to create accompanying visuals for each point.
Debugging complex LLM prompts
An AI developer is building a new feature. Instead of setting up local API keys and running manual test scripts, they use the chat tool to talk to the server, testing different prompts and inference parameters instantly. They can then use list_models to confirm the best model for production.
Processing user-uploaded audio data
A product team gets a user-submitted podcast clip. They pass the public URL to the agent, which calls the transcribe tool. The agent receives the clean text, which they can then immediately pass to the embed tool for indexing into their internal knowledge base.
The Tradeoffs
Calling separate APIs for each step
Trying to transcribe a podcast, then embedding the text, and then chatting with the results requires three separate API calls, managing three different authentication flows and three different data types.
→
Use the Fireworks AI MCP Server. Let your agent call transcribe first. Feed the resulting text into the embed tool. Finally, pass the resulting vectors to the chat tool context. Keep it in one flow.
Ignoring model availability
Writing code that assumes a model name will work, only to fail at runtime because the developer missed a version update or the model was deprecated.
→
Always use the list_models tool first. This lets your agent query the server and confirm the exact, available model IDs and versions before running any task.
Manually formatting inputs
Having to manually extract text from a file, clean it, and format it into an array before sending it to an embedding service.
→
Use the embed tool. It accepts an array of strings directly, simplifying the input process and handling the vector synthesis for you.
When It Fits, When It Doesn't
Use this server if your workflow requires mixing modalities: text chat, image generation, audio transcription, and vector embedding. You need a single point of access that handles complex orchestration. Don't use this if you only need basic text completion; those tasks are simple enough for most dedicated text APIs. If your workflow is purely data-focused (e.g., just reading from a database), you don't need it. But if your workflow involves 'take this file, analyze it, and then draw a picture of it,' this is the right place. The chat tool acts as the central brain, calling the others when needed.
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.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Copy-pasting transcript text and embedding it manually is a nightmare.
Right now, if you get a long meeting recording, you have to download the audio, use a separate service to transcribe it, copy the resulting text, and then feed that text into a different service just to generate embeddings. You're dealing with three different file formats and three different pipelines.
With the Fireworks AI MCP Server, you just point your agent at the audio URL, call `transcribe`. The resulting text is clean, and you immediately pass that output to the `embed` tool. The entire process happens within your agent's single, continuous conversation.
Fireworks AI MCP Server: Generate visuals and media.
Before this server, creating a visual asset required jumping to a separate image generation API, managing a second key, and sending the prompt in a different format. It was a manual handoff of data and context.
Now, you just ask the agent to 'Generate a cyberpunk forest at night.' The agent calls the `image` tool, handles the inference, and gives you the high-fidelity result—no context switching required.
Common Questions About Fireworks AI MCP
How does the Fireworks AI MCP Server handle multiple model types? +
The list_models tool lets your agent check all available models. This ensures you use the fastest or most accurate model for the job before running a task like chat or completion.
Can I transcribe audio and then embed the text using the Fireworks AI MCP Server? +
Yes. Your agent calls transcribe with the URL, gets the text, and then immediately passes that text to the embed tool. It chains the process seamlessly.
Is the `chat` tool the only way to use Fireworks AI? +
No. While chat is the primary orchestration tool, you can also call specific tools directly, like image or embed, if your agent needs to execute a function without a conversational wrapper.
What is the difference between `chat` and `completion` in Fireworks AI? +
The chat tool manages multi-turn conversations, remembering context across multiple messages. The completion tool is for single, stateless text generations, like finishing a paragraph.
What kind of data does the `image` tool accept? +
The image tool accepts a text prompt (a string). It doesn't require file uploads; the agent handles the prompt string for image generation.
How do I handle rate limits when using the `chat` tool? +
The server handles rate limits using standard exponential backoff logic. If your calls exceed the allotted rate, your AI client will automatically retry the request after a calculated delay. You only need to monitor your usage dashboard.
Can I use the `list_models` tool to check which models are available for `completion`? +
Yes, the list_models tool provides a comprehensive list of all available model IDs and versions. You can run this first to confirm the exact model name you want to use for text completion.
What data types are supported when I use the `embed` tool? +
The embed tool accepts arrays of strings as input. It generates multi-dimensional vector representations for each string in the array. These vectors are ready for semantic search or indexing in your vector database.
Can my agent perform semantic searches using Fireworks AI embeddings? +
Yes. Use the 'embed' tool. Provide a JSON array of text strings, and the agent will retrieve multi-dimensional vector representations. You can then use these vectors to perform semantic similarity matches within your database.
How do I list all available LLM and image models via chat? +
Use the 'list_models' tool. Your agent will enumerate the high-speed open-source and proprietary models hosted by Fireworks AI, providing the IDs and versions needed for your inference requests.
Can I generate high-fidelity images through the agent using Fireworks AI? +
Absolutely. Use the 'image' tool. Provide your text prompt, and the agent will command synchronous inference against Fireworks-hosted image models to deliver high-quality visual content natively.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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