Fireworks AI

@fireworks list all available models in my account and tell me which ones support function calling

Found 12 models in your Fireworks account. Function-calling capable models include: accounts/fireworks/models/firefunction-v2 (optimized for tool use, 16k context), accounts/fireworks/models/llama-v3p1-70b-instruct (general purpose, 128k context), and accounts/fireworks/models/qwen2p5-72b-instruct (multilingual, 32k context). The remaining 9 models are embedding models or base models without function-calling support. All function-calling models show 'serving' status and are ready for inference.

This example demonstrates model discovery and metadata inspection. The MCP returns raw model objects; the AI synthesizes capability information from model IDs and metadata fields. Requires read access to the account's model catalog. Model availability can change if models are archived or if account quotas shift.

@fireworks create embeddings for these three product descriptions using your fastest embedding model, then calculate cosine similarity between them

Generated embeddings using accounts/fireworks/models/nomic-embed-text-v1.5 (768 dimensions, 8k context). Embedding vectors created for all three inputs in 340ms. Cosine similarity scores: Description A ↔ B = 0.847 (high similarity, likely same product category), A ↔ C = 0.412 (moderate similarity), B ↔ C = 0.389 (moderate similarity). Descriptions A and B share significant semantic overlap while C appears to describe a different product type.

Showcases the embedding generation tool paired with AI reasoning for semantic analysis. The MCP returns raw vector arrays; the AI performs the similarity calculation. Embedding model selection affects dimensionality and performance. Rate limits apply to batch embedding requests—large document sets may require chunking across multiple calls.

@fireworks check the serving status of my llama-v3p3-70b-instruct deployment and generate a test completion to verify it's working

Deployment accounts/fireworks/deployments/llama-v3p3-70b-prod shows status: SERVING, with 2 replicas active and average response time 1.2s over the last hour. Test completion successful—model responded to 'Explain quantum entanglement in one sentence' with: 'Quantum entanglement is a phenomenon where two particles become connected such that the quantum state of one instantly influences the other, regardless of distance.' Deployment is healthy and serving traffic normally.

Combines deployment inspection with live inference testing. Demonstrates both read-only monitoring (Get Deployment) and write operations (Create Chat Completion). Useful for health checks before routing production traffic. Note that deployment status can lag real-time serving capacity by 30-60 seconds, and test completions consume account quota.

When Fireworks wins for fast embedding and LLM iteration

A 3-person ML team building a document Q&A prototype needs to test five different embedding models and three chat models in the same afternoon. Fireworks MCP is the right call here because the Create Embeddings and Create Chat Completion tools let you swap model IDs without changing code, and the List Models tool surfaces every option in your account. The API-key auth means no OAuth dance slowing down your sprint. The trade-off: if you're already locked into OpenAI or Anthropic for production, adding a third vendor just for prototyping creates deployment friction later. Use Fireworks when your team values model diversity over vendor consolidation, or when you're genuinely unsure which embedding approach will win.

When this MCP fits a single-vendor inference stack

A 6-person support-ops team at a SaaS company runs a knowledge-base search tool that embeds tickets nightly and answers agent queries in real time. They picked Fireworks for cost and speed, so the embedding and chat models already live there. The MCP makes sense because the team's Slack bot can call Create Embeddings and Create Chat Completion directly from shared prompts without maintaining a separate API client. The Get Deployment tool lets the ops lead check serving status when latency spikes. The boundary: if your stack mixes Fireworks embeddings with Anthropic chat, you're better off with a multi-provider MCP or raw SDKs. Fireworks MCP shines when you've committed to one inference vendor and want agent-native access.

When Generate Model Response beats raw chat completions

A 4-person automation team is building an agent that books meeting rooms by calling internal APIs. The Generate Model Response tool supports function calling and optional response storage, so the agent can chain tool invocations without the team writing a state machine. This is faster than stitching together Create Chat Completion calls and managing conversation history in your own database. The catch: the Responses API is Fireworks-specific, so you're locked in if you later want to move the orchestration layer to LangGraph or a different provider. Use this MCP when you're prototyping agent workflows quickly and Fireworks is already your inference layer, not when you need vendor-agnostic orchestration from day one.