Forecast MCP Server for AutoGen 6 tools — connect in under 2 minutes
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Forecast as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="forecast_agent",
tools=tools,
system_message=(
"You help users with Forecast. "
"6 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Forecast MCP Server
Connect your Forecast.app account to any AI agent and take full control of your resource management and project scheduling through natural conversation.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Forecast tools. Connect 6 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
What you can do
- Project Orchestration — Retrieve the global array of all managed projects and fetch comprehensive scheduling and resource states belonging to specific project IDs natively
- Task Lifecycle Auditing — Enumerate specific physical tasks allocated under project IDs to track work completion and identify bottlenecks synchronously
- Personnel Oversight — Fetch physical identity definitions and availability constraints of global members to manage team utilization and workload limits securely
- Client Relationship Mapping — Extract explicit client relationships mapped to projects inside your account to manage stakeholder communications flawlessly
- Milestone Tracking — Identify timebox markers bounding specific sprint or deliverable targets to ensure project timelines remain within active boundaries
- Resource Allocation Discovery — Analyze specific localized variables decoding active assignments and extracting hidden structural constraints across your portfolio
- Operational Metadata retrieval — Access global account metadata and project-level attributes to verify workspace configurations natively
The Forecast MCP Server exposes 6 tools through the Vinkius. Connect it to AutoGen in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Forecast to AutoGen via MCP
Follow these steps to integrate the Forecast MCP Server with AutoGen.
Install AutoGen
Run pip install "autogen-ext[mcp]"
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Integrate into workflow
Use the agent in your AutoGen multi-agent orchestration
Explore tools
The workbench discovers 6 tools from Forecast automatically
Why Use AutoGen with the Forecast MCP Server
AutoGen provides unique advantages when paired with Forecast through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Forecast tools to solve complex tasks
Role-based architecture lets you assign Forecast tool access to specific agents. a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive Forecast tool calls
Code execution sandbox: AutoGen agents can write and run code that processes Forecast tool responses in an isolated environment
Forecast + AutoGen Use Cases
Practical scenarios where AutoGen combined with the Forecast MCP Server delivers measurable value.
Collaborative analysis: one agent queries Forecast while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from Forecast, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using Forecast data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process Forecast responses in a sandboxed execution environment
Forecast MCP Tools for AutoGen (6)
These 6 tools become available when you connect Forecast to AutoGen via MCP:
get_project
Get project details
list_clients
List clients
list_milestones
List milestones
list_people
List people
list_projects
List projects
list_tasks
List tasks
Example Prompts for Forecast in AutoGen
Ready-to-use prompts you can give your AutoGen agent to start working with Forecast immediately.
"List all active projects in Forecast"
"Show me the tasks for project 'API V2 Development'"
"Who is available this week for a new assignment?"
Troubleshooting Forecast MCP Server with AutoGen
Common issues when connecting Forecast to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"Forecast + AutoGen FAQ
Common questions about integrating Forecast MCP Server with AutoGen.
How does AutoGen connect to MCP servers?
Can different agents have different MCP tool access?
Does AutoGen support human approval for tool calls?
Connect Forecast with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Forecast to AutoGen
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.
