How do I use multiple MCP servers (Firecrawl Deep Research, Microsoft Teams) with Fireworks AI?

Fireworks AI supports connecting to multiple MCP servers simultaneously. You can configure each Firecrawl Deep Research, Microsoft Teams server independently and use their tools together in your AI applications. This enables powerful multi-source AI systems where your models can access data from Firecrawl Deep Research, Microsoft Teams and combine insights for comprehensive responses. Klavis AI simplifies multi-server management with centralized configuration and unified connection handling for Fireworks AI applications.

What are the benefits of using Firecrawl Deep Research and Microsoft Teams together with Fireworks AI?

Combining Firecrawl Deep Research and Microsoft Teams with your Fireworks AI applications creates powerful synergies for advanced AI systems. Each service brings unique data sources and capabilities that complement each other, enabling comprehensive information retrieval, cross-platform data correlation, and enhanced AI responses with multi-source validation using Fireworks AI's high-performance inference.

Do I need special packages for Fireworks AI MCP server integration?

Yes, you'll need the Fireworks AI SDK and any service-specific packages for the MCP servers you want to connect to. Klavis AI provides detailed package requirements and automated installation scripts for each supported MCP server, along with Fireworks AI-specific configuration examples and troubleshooting guides to ensure smooth setup.

Can I use function calling with Fireworks AI and MCP servers?

Absolutely! MCP servers are designed to work seamlessly with Fireworks AI's function calling capabilities. You can use MCP server tools with any Fireworks AI model that supports function calling, allowing your AI models to perform actions and retrieve data from external services as part of their reasoning and response generation process.

How do I handle authentication for MCP servers with Fireworks AI?

Authentication for MCP servers with Fireworks AI is handled through environment variables and configuration files. Each MCP server may require different authentication methods (API keys, OAuth tokens, etc.). Klavis AI handles the authentication flow automatically once you provide the necessary credentials, and provides secure credential management, encrypted storage, and pre-configured authentication templates for all supported services.

Which Fireworks AI models are best for these MCP servers?

For these MCP servers, we recommend using Fireworks AI models with strong function calling capabilities such as Llama 3.1 70B, Mixtral 8x7B, or Code Llama for coding tasks. These models provide excellent performance for tool use and can effectively leverage the capabilities of external MCP servers. Klavis AI provides model-specific optimization guides and performance benchmarks to help you choose the best Fireworks AI model for your use case.

What types of AI applications benefit most from Fireworks AI + MCP server integration?

AI applications that benefit most include: chatbots and virtual assistants needing real-time data access, automated workflow systems that interact with multiple platforms, content generation tools that need to pull data from various sources, and AI agents that perform complex multi-step tasks. Fireworks AI's high-performance inference combined with MCP servers extends your applications beyond static responses to dynamic, real-time AI interactions.

How does MCP server integration affect Fireworks AI performance?

MCP server integration adds minimal latency when properly configured, thanks to Fireworks AI's optimized inference and efficient function calling. The impact is typically negligible compared to the enhanced capabilities gained. Best practices include caching frequently accessed data, using appropriate timeouts, and implementing fallback strategies for when external services are unavailable. Klavis AI provides performance monitoring tools and optimization recommendations to ensure your Fireworks AI + MCP server applications run at peak efficiency.

Can I use streaming responses with Fireworks AI and MCP servers?

Yes, Fireworks AI's streaming capabilities work well with MCP server integrations. You can stream AI responses while making function calls to MCP servers, providing a smooth user experience. The streaming continues after function calls are completed, allowing for real-time updates and interactive AI experiences. Klavis AI provides examples and best practices for implementing streaming with MCP server function calls.

Why choose Klavis AI for Fireworks AI MCP server connections?

Klavis AI provides the most comprehensive platform for Fireworks AI MCP server integration with features like pre-built Fireworks AI connectors, automated setup scripts, detailed documentation with Fireworks AI-specific examples, secure credential management, and performance optimization guides. Our platform is specifically designed to make Fireworks AI + MCP server integration as seamless as possible for developers building advanced AI applications. With Klavis AI's centralized management dashboard, you can monitor all your MCP server connections, track performance metrics, analyze function call usage, and troubleshoot issues from a single interface.

Connectto Firecrawl Deep Research, Microsoft Teams MCP Servers

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Firecrawl Deep Research

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A personal research assistant that analyze sources across the web, based on Firecrawl

Available Tools:

firecrawl_deep_research

Microsoft Teams

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Microsoft Teams is a collaboration platform that combines workplace chat, meetings, calling, and file sharing. Manage teams, channels, messages, meetings, and collaborate with your organization seamlessly

Quick Setup Guide

Follow these steps to connect Fireworks AI to these MCP servers

Create Your Account

Configure Connections

Add your desired MCP servers to Fireworks AI and configure authentication settings.

Test & Deploy

Verify your connections work correctly and start using your enhanced AI capabilities.

Fireworks AI + KlavisAI Integration Snippets

View Documentation

import os
import json
from fireworks.client import Fireworks
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat

# Initialize clients
fireworks_client = Fireworks(api_key=os.getenv("FIREWORKS_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

firecrawl_deep_research_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.FIRECRAWL_DEEP_RESEARCH,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

microsoft_teams_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.MICROSOFT_TEAMS,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

# Get all MCP tools
firecrawl_deep_research_tools = klavis_client.mcp_server.list_tools(
    server_url=firecrawl_deep_research_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.OPENAI,
)
microsoft_teams_tools = klavis_client.mcp_server.list_tools(
    server_url=microsoft_teams_mcp_instance.server_url,
    connection_type=ConnectionType.STREAMABLE_HTTP,
    format=ToolFormat.OPENAI,
)

# Combine all tools
all_tools = []
all_tools.extend(firecrawl_deep_research_tools.tools)
all_tools.extend(microsoft_teams_tools.tools)

messages = [
    {"role": "system", "content": "You are a helpful assistant with access to multiple data sources."},
    {"role": "user", "content": user_message}
]

response = fireworks_client.chat.completions.create(
    model="accounts/fireworks/models/llama-v3p1-70b-instruct",
    messages=messages,
    tools=all_tools
)

Frequently Asked Questions

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ConnectFireworks AIto Firecrawl Deep Research, Microsoft Teams MCP Servers