Can my agent list all active projects in Forecast?

Yes. Use the 'list_projects' tool. The agent will retrieve the global array of all managed projects mapped inside your Forecast account, including their names and unique IDs.

How do I check a specific team member's availability via chat?

Use the 'list_people' tool. Your agent will fetch the physical identity definitions and availability constraints of global members, allowing you to monitor team utilization and workload natively.

Can I see the upcoming milestones for a project through the agent?

Absolutely. Use the 'list_milestones' tool with the specific Project ID. The agent will identify timebox markers bounding specific sprint or deliverable targets for that project natively.

Forecast MCP Server for LlamaIndex 6 tools — connect in under 2 minutes

Name: Forecast
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Forecast as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Forecast. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Forecast?"
    )
    print(response)

asyncio.run(main())

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* 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.

LlamaIndex agents combine Forecast tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

Follow these steps to integrate the Forecast MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Forecast

Why Use LlamaIndex with the Forecast MCP Server

LlamaIndex provides unique advantages when paired with Forecast through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Forecast tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Forecast tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Forecast, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Forecast tools were called, what data was returned, and how it influenced the final answer

Forecast + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Forecast MCP Server delivers measurable value.

Hybrid search: combine Forecast real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Forecast to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Forecast for fresh data

Analytical workflows: chain Forecast queries with LlamaIndex's data connectors to build multi-source analytical reports

Forecast MCP Tools for LlamaIndex (6)

These 6 tools become available when you connect Forecast to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

Common issues when connecting Forecast to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Forecast + LlamaIndex FAQ

Common questions about integrating Forecast MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Forecast tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.