How can Linkup help my agent provide more up-to-date answers?

Use the `linkup_search` tool to give your agent access to live web data. By performing semantic searches across the internet, your agent can retrieve the latest news, reports, and documentation, grounding its answers in current facts.

Can I extract clean text from a specific URL for RAG?

Yes. The `linkup_fetch` tool is specifically designed for content extraction. It renders the target page and returns a clean text version stripped of navigation and ads, making it ideal for feeding high-quality context to your agent.

What is the difference between standard and deep search modes?

Standard search focuses on rapid fact-finding and top results. Deep search performs a more comprehensive crawl across many more sources, which is better for complex research tasks that require diverse perspectives and detailed data.

Linkup (AI Search & RAG) MCP Server for CrewAI 2 tools — connect in under 2 minutes

Name: Linkup (AI Search & RAG)
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 2 Tools Framework

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

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Linkup (AI Search & RAG) Specialist",
    goal="Help users interact with Linkup (AI Search & RAG) effectively",
    backstory=(
        "You are an expert at leveraging Linkup (AI Search & RAG) 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 Linkup (AI Search & RAG) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 2 available tools "
        "and what they can do."
    ),
)

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

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About Linkup (AI Search & RAG) MCP Server

Connect your Linkup account to any AI agent and take full control of real-time web intelligence and content retrieval for RAG pipelines through natural conversation.

When paired with CrewAI, Linkup (AI Search & RAG) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Linkup (AI Search & RAG) 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

Semantic Web Search — Execute context-rich queries that return high-relevancy results specifically optimized for Large Language Models directly from your agent
Deep Content Retrieval — Extract clean, readable text from any web URL, stripping away noise and navigation to feed high-quality grounding data to your AI
RAG-Ready Payloads — Retrieve structured search results including titles, snippets, and source URLs designed for seamless integration into vector stores
Precision Extraction — Target specific URLs for content parsing, ensuring your agent has the exact technical context or documentation required for its task
Real-time Intelligence — Access the latest facts and data from across the internet to ground your agent's answers in up-to-date reality
Search Breadth — Switch between standard and deep search modes to balance between rapid fact-finding and comprehensive research across the web

The Linkup (AI Search & RAG) MCP Server exposes 2 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 Linkup (AI Search & RAG) to CrewAI via MCP

Follow these steps to integrate the Linkup (AI Search & RAG) 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 2 tools from Linkup (AI Search & RAG)

Why Use CrewAI with the Linkup (AI Search & RAG) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Linkup (AI Search & RAG) 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

Linkup (AI Search & RAG) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Linkup (AI Search & RAG) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Linkup (AI Search & RAG) 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 Linkup (AI Search & RAG), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Linkup (AI Search & RAG) 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 Linkup (AI Search & RAG) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Linkup (AI Search & RAG) MCP Tools for CrewAI (2)

These 2 tools become available when you connect Linkup (AI Search & RAG) to CrewAI via MCP:

fetch_url

Bypasses advanced bot protections executing complex SPA JavaScript loops automatically. Fetch and extract clean content from any specific URL using Linkup Platform

search_web

Choose "fast" mapping for basic factual requests and "deep" for thorough research limits. Perform a real-time web search extracting deep answers utilizing Linkup Platform

Example Prompts for Linkup (AI Search & RAG) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Linkup (AI Search & RAG) immediately.

"Search for the latest NVIDIA earnings report summary"

"Extract the technical specifications from this documentation URL: [url]"

"Deep search for 'AI agent security best practices 2024'"

Troubleshooting Linkup (AI Search & RAG) MCP Server with CrewAI

Common issues when connecting Linkup (AI Search & RAG) 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.

Linkup (AI Search & RAG) + CrewAI FAQ

Common questions about integrating Linkup (AI Search & RAG) 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.