How do I find my Aha! API Key?

Log in to your Aha! account, click on your profile avatar, and select **Personal settings**. Go to the **Developer** tab and click **API keys** to generate or retrieve your key.

Can I update a feature status via the agent?

Yes! Use the `update_feature` tool and provide the Feature ID along with the new status (e.g., 'Shipped', 'In development'). Your agent will synchronize the change with your roadmap.

Does this support creating new ideas?

Yes, you can use the `create_idea` tool to capture feedback or suggestions. Provide a title and description, and it will be added to your ideation portal.

Aha! MCP Server for LlamaIndex 5 tools — connect in under 2 minutes

Name: Aha!
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 5 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Aha! 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 Aha!. "
            "You have 5 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Aha!?"
    )
    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 Aha! MCP Server

Connect your Aha! account to your AI agent to unlock professional product management and roadmap orchestration. From capturing new product ideas to auditing technical metadata for features and tracking strategic initiatives, your agent handles your product lifecycle through natural conversation.

LlamaIndex agents combine Aha! tool responses with indexed documents for comprehensive, grounded answers. Connect 5 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

Feature Orchestration — List and retrieve details for features, update statuses, and audit requirement hierarchies
Idea Management — List and create product ideas to ensure customer feedback is always captured and categorized
Strategic Oversight — Monitor high-level goals and initiatives to ensure your team is aligned with the product vision
Release Tracking — Retrieve details on upcoming product releases and associated work items across your portfolios
Product Insights — Quickly identify feature bottlenecks or unvoted ideas directly from your chat interface

The Aha! MCP Server exposes 5 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 Aha! to LlamaIndex via MCP

Follow these steps to integrate the Aha! 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 5 tools from Aha!

Why Use LlamaIndex with the Aha! MCP Server

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

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

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

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

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

Aha! + LlamaIndex Use Cases

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

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

Data enrichment: query Aha! 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 Aha! for fresh data

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

Aha! MCP Tools for LlamaIndex (5)

These 5 tools become available when you connect Aha! to LlamaIndex via MCP:

create_idea

Capture a new product idea

get_feature

Get feature details

list_features

List product features

list_ideas

List product ideas

list_releases

List product releases

Example Prompts for Aha! in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Aha! immediately.

"List all active features in my 'Web App' product."

"Create a new idea named 'Dark Mode Support' with description 'User requested dark theme for better accessibility'."

"Show me the details for feature ID 'APP-F-101'."

Troubleshooting Aha! MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Aha! + LlamaIndex FAQ

Common questions about integrating Aha! 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.