IBGE Nomes MCP Server for CrewAIGive CrewAI instant access to 2 tools to Get Name Frequency and Get Names Ranking

Name: IBGE Nomes
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

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

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The IBGE Nomes MCP Server for CrewAI is a standout in the Data Analytics category — giving your AI agent 2 tools to work with, ready to go from day one.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="IBGE Nomes Specialist",
    goal="Help users interact with IBGE Nomes effectively",
    backstory=(
        "You are an expert at leveraging IBGE Nomes 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 IBGE Nomes "
        "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 IBGE Nomes MCP Server

Connect to the IBGE (Brazilian Institute of Geography and Statistics) database through any AI agent to explore the rich demographic history of Brazilian names. This server provides direct access to the 'Nomes no Brasil' census data.

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

Name Frequency — Query the number of births per decade for specific names (e.g., 'MARIA' or 'ENZO') to see how trends evolved over time.
Rankings & Popularity — Generate rankings of the most frequent names in Brazil, with optional filters for gender and specific decades.
Geographic Insights — Filter results by locality ID to understand regional naming preferences across different Brazilian states and municipalities.
Comparative Analysis — Use the pipe separator to compare multiple names simultaneously and identify cultural shifts.

The IBGE Nomes MCP Server exposes 2 tools through the Vinkius. Connect it to CrewAI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 2 IBGE Nomes tools available for CrewAI

When CrewAI connects to IBGE Nomes through Vinkius, your AI agent gets direct access to every tool listed below — spanning demographics, brazil, census, 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.

get

Get name frequency on IBGE Nomes

Multiple names can be separated by a pipe (|). Obtains the frequency of births per decade for a specific name

get

Get names ranking on IBGE Nomes

Obtains a ranking of the most frequent names according to specified filters

Connect IBGE Nomes to CrewAI via MCP

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

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 IBGE Nomes

Why Use CrewAI with the IBGE Nomes MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with IBGE Nomes 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

IBGE Nomes + CrewAI Use Cases

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

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

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

Example Prompts for IBGE Nomes in CrewAI

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

"What is the birth frequency of the name 'Neymar' in Brazil per decade?"

"Show me the top 10 most popular female names in Brazil during the 1980s."

"Compare the popularity of 'Enzo' and 'Valentina' using IBGE data."

Troubleshooting IBGE Nomes MCP Server with CrewAI

Common issues when connecting IBGE Nomes to CrewAI through 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.

IBGE Nomes + CrewAI FAQ

Common questions about integrating IBGE Nomes 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.