How do I find my Pipeliner Space ID and Service URL?

Log in to Pipeliner, go to **Admin Space** > **API**. There you can find your unique Space ID and the specific API Service URL for your instance.

Can I see opportunities across multiple pipelines?

Yes! Use the `list_pipeliner_opportunities` tool to retrieve a full list of deals. You can then ask the AI agent to filter or group them by pipeline name.

Does this support looking up individual user activities?

Absolutely. Use the `list_pipeliner_activities` tool to retrieve a log of all sales tasks, calls, and meetings recorded in your CRM space.

Pipeliner MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Pipeliner
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Pipeliner through Vinkius, pass the Edge URL in the `mcps` parameter and every Pipeliner tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Pipeliner Specialist",
    goal="Help users interact with Pipeliner effectively",
    backstory=(
        "You are an expert at leveraging Pipeliner tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Pipeliner "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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About Pipeliner MCP Server

Connect your Pipeliner CRM space to any AI agent and take full control of your sales ecosystem through natural conversation.

When paired with CrewAI, Pipeliner becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Pipeliner tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Lead & Opportunity Oversight — List and retrieve detailed metadata for leads and sales opportunities across your workspace.
Sales Pipeline Management — List available pipelines and track the progress of deals through different stages.
Workforce Visibility — List company accounts, business contacts, and team members to maintain a clear view of your stakeholders.
Activity & Task Tracking — Monitor sales activities and assigned tasks to ensure your team stays productive.
Detailed Entity Inspections — Get deep-dive details for any specific lead or opportunity to understand its full history.

The Pipeliner MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI 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 Pipeliner to CrewAI via MCP

Follow these steps to integrate the Pipeliner MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Pipeliner

Why Use CrewAI with the Pipeliner MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Pipeliner through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Pipeliner + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Pipeliner MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Pipeliner for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Pipeliner, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Pipeliner tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Pipeliner against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Pipeliner MCP Tools for CrewAI (10)

These 10 tools become available when you connect Pipeliner to CrewAI via MCP:

get_pipeliner_lead

Get details for a specific lead

get_pipeliner_opportunity

Get details for a specific opportunity

list_pipeliner_accounts

List all company accounts

list_pipeliner_activities

List sales activities and tasks

list_pipeliner_contacts

List all business contacts

list_pipeliner_leads

List all sales leads

list_pipeliner_opportunities

List all sales opportunities

list_pipeliner_pipelines

List available sales pipelines

list_pipeliner_tasks

List all assigned tasks

list_pipeliner_users

List users in the Pipeliner space

Example Prompts for Pipeliner in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Pipeliner immediately.

"List all sales opportunities in the 'Enterprise' pipeline."

"Show me the last 5 leads added to Pipeliner."

"What are my sales activities for this week?"

Troubleshooting Pipeliner MCP Server with CrewAI

Common issues when connecting Pipeliner to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Pipeliner + CrewAI FAQ

Common questions about integrating Pipeliner MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.