Connectto Asana, Perplexity, Klavis ReportGen MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Asana, Perplexity, Klavis ReportGen for enhanced automation capabilities in Klavis AI.
Asana
Asana is a web and mobile application designed to help teams organize, track, and manage their work. It provides project management tools, task assignment, collaboration features, and progress tracking to boost team productivity
Available Tools:
- asana_create_task
- asana_get_task
- asana_search_tasks
- +16 more tools
Perplexity
Perplexity is an AI research assistant that provides accurate answers and cites sources
Klavis ReportGen
Generate visually appealing JavaScript web reports from search queries with Klavis AI.
Available Tools:
- generate_web_reports
Quick Setup Guide
Follow these steps to connect Fireworks AI to these MCP servers
Create Your Account
Sign up for KlavisAI to access our MCP server management platform.
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
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"))
asana_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.ASANA,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
perplexity_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.PERPLEXITY,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
klavis_reportgen_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.KLAVIS_REPORTGEN,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Get all MCP tools
asana_tools = klavis_client.mcp_server.list_tools(
server_url=asana_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
perplexity_tools = klavis_client.mcp_server.list_tools(
server_url=perplexity_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
klavis_reportgen_tools = klavis_client.mcp_server.list_tools(
server_url=klavis_reportgen_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
# Combine all tools
all_tools = []
all_tools.extend(asana_tools.tools)
all_tools.extend(perplexity_tools.tools)
all_tools.extend(klavis_reportgen_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
Everything you need to know about connecting to these MCP servers
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