Can the agent monitor a long-running relevance AI agent task?

Yes. You can trigger an agent using `trigger_agent`, and because it provides a `run_id`, you can explicitly prompt your local Assistant to periodically "check in on the status using `get_agent_run` every minute until finished" or ask it to summarize the step-by-step agent logs after completion.

What is the differences between tasks, tools, and agents in Relevance AI?

Agents are autonomous workers capable of making step-by-step reasoning choices based on instructions and tools. Tasks are linear, pre-chained sets of commands and prompts. Tools (`list_tools`) are the individual capabilities, like a custom API integration or web scraper, that tasks and agents utilize to perform their actions.

How do I find my specific Region and Project ID?

These details are typically nested within the URL string when you are logged into your workspace or found globally in your developer API keys configuration pane inside your Relevance AI team dashboard. The Region is usually something like 'us-east-1' or 'v2'.

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

Name: Relevance AI
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Relevance AI through Vinkius, pass the Edge URL in the `mcps` parameter and every Relevance AI 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

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Relevance AI Specialist",
    goal="Help users interact with Relevance AI effectively",
    backstory=(
        "You are an expert at leveraging Relevance AI 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 Relevance AI "
        "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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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Relevance AI MCP Server

Connect your conversational AI to your Relevance AI workspace. By wrapping your custom agents, datasets, and API tools into this MCP extension, you transform your chat interface into a command center for orchestrating complex, autonomous AI operations and large-scale data workflows.

When paired with CrewAI, Relevance AI becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Relevance AI 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

Orchestrate Agents — Command your pre-built autonomous agents to execute tasks (trigger_agent). Monitor their progress and read their exact reasoning steps dynamically (get_agent_run). Use list_agents to discover all available AI worker configurations.
Execute Tasks & Workflows — Trigger predefined chained prompts or specific micro-tasks without leaving your chat (trigger_task), scaling repetitive workflows reliably.
Manage Knowledge Datasets — Take full control of your vector databases and tables. Insert new rows of knowledge directly from conversational context (insert_documents), retrieve raw unstructured data entries (get_documents), or surgically delete obsolete knowledge base items (delete_documents).

The Relevance AI 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 Relevance AI to CrewAI via MCP

Follow these steps to integrate the Relevance AI 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 Relevance AI

Why Use CrewAI with the Relevance AI MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Relevance AI 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

Relevance AI + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Relevance AI 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 Relevance AI, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Relevance AI 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 Relevance AI against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Relevance AI MCP Tools for CrewAI (10)

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

delete_documents

This action is irreversible. Deletes documents from a dataset by their IDs

get_agent_run

Retrieves the status and logs of a specific agent run

get_documents

Retrieves documents from a dataset

insert_documents

Provide documents as a JSON array of objects. Inserts documents into a dataset

list_agents

Lists all AI agents in the Relevance AI studio

list_datasets

Lists all datasets (knowledge tables) in the project

list_tasks

Lists all tasks (chained prompts) in the studio

list_tools

Lists all custom tools registered in the studio

trigger_agent

Provide inputs as a JSON object. Triggers an AI agent execution

trigger_task

Triggers a specific task execution

Example Prompts for Relevance AI in CrewAI

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

"List all available agents in my Relevance AI Studio and their IDs."

"Start a run for the 'Market Analysis' agent passing `{"company": "OpenAI"}` as the payload, then tell me the Run ID."

"Insert this JSON array of top competitor articles into the 'competitor_docs' dataset."

Troubleshooting Relevance AI MCP Server with CrewAI

Common issues when connecting Relevance AI 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.

Relevance AI + CrewAI FAQ

Common questions about integrating Relevance AI 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.