Activepieces MCP Server for LlamaIndexGive LlamaIndex instant access to 32 tools to Add Piece, Apply Flow Operation, Configure Git Repo, and more

Name: Activepieces
Price: 29 USD
Availability: InStock
Rating: 4.9 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

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

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The Activepieces MCP Server for LlamaIndex is a standout in the Productivity category — giving your AI agent 32 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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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 Activepieces. "
            "You have 32 tools available."
        ),
    )

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

Connect your Activepieces account to any AI agent to orchestrate complex automations and monitor your business workflows through natural language.

LlamaIndex agents combine Activepieces tool responses with indexed documents for comprehensive, grounded answers. Connect 32 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

Flow Management — List, create, retrieve, and delete automation flows within your projects using list_flows and create_flow.
Execution Monitoring — Track flow runs, check statuses, and inspect detailed step results for debugging with list_flow_runs and get_flow_run.
App Connections — Manage credentials and connections for external services like Slack, Discord, or Google Sheets via list_app_connections.
Flow Operations — Apply structural changes or status updates to existing flows programmatically using apply_flow_operation.
Organization — List and manage folders to keep your automation workspace tidy with list_folders.

The Activepieces MCP Server exposes 32 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 32 Activepieces tools available for LlamaIndex

When LlamaIndex connects to Activepieces through Vinkius, your AI agent gets direct access to every tool listed below — spanning workflow-automation, no-code, business-process, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

add

Add piece on Activepieces

Add a custom piece to the platform

apply

Apply flow operation on Activepieces

g., MOVE_ACTION, CHANGE_STATUS). Apply an operation to a flow

configure

Configure git repo on Activepieces

Configure Git sync for a project

create

Create flow on Activepieces

Create a new flow

create

Create folder on Activepieces

Create a new folder

create

Create project on Activepieces

Create a new project

create

Create project release on Activepieces

Create a project release

delete

Delete app connection on Activepieces

Delete an app connection

delete

Delete flow on Activepieces

Delete a flow by ID

delete

Delete folder on Activepieces

Delete a folder

delete

Delete global connection on Activepieces

Delete a global connection

delete

Delete project member on Activepieces

Remove a member from a project

get

Get flow on Activepieces

Get a specific flow by ID

get

Get flow run on Activepieces

Get detailed execution data for a flow run

get

Get mcp server on Activepieces

Get MCP server configuration for AI assistants

invite

Invite user on Activepieces

Invite a user to the platform or project

list

List app connections on Activepieces

List app connections

list

List flow runs on Activepieces

List flow runs

list

List flows on Activepieces

List automation flows

list

List folders on Activepieces

List folders

list

List global connections on Activepieces

List global connections

list

List project members on Activepieces

List members of a project

list

List projects on Activepieces

List projects

list

List records on Activepieces

List records in a table

list

List tables on Activepieces

List internal data tables

list

List users on Activepieces

List users

rotate

Rotate mcp token on Activepieces

Rotate MCP token for a project

update

Update folder on Activepieces

Update a folder name

update

Update project on Activepieces

Update project settings

update

Update record on Activepieces

Update a specific record

upsert

Upsert app connection on Activepieces

Supports SECRET_TEXT, OAUTH2, BASIC_AUTH, CUSTOM_AUTH, etc. Create or update an app connection

upsert

Upsert global connection on Activepieces

Create or update a global connection

Connect Activepieces to LlamaIndex via MCP

Follow these steps to wire Activepieces into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

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 32 tools from Activepieces

Why Use LlamaIndex with the Activepieces MCP Server

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

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

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

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

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

Activepieces + LlamaIndex Use Cases

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

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

Data enrichment: query Activepieces 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 Activepieces for fresh data

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

Example Prompts for Activepieces in LlamaIndex

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

"List all active automation flows in project 'proj_123'."

"Show me the last 5 runs for flow ID 'flow_1'."

"Create a new flow named 'Customer Support Sync' in project 'proj_123'."

Troubleshooting Activepieces MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Activepieces + LlamaIndex FAQ

Common questions about integrating Activepieces 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 Activepieces tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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