How to Use the Mato Grosso do Sul Open Data MCP in CrewAI

Q: Can CrewAI agents write their own SQL queries for Mato Grosso do Sul Open Data?

Yes, if you give them access to datastoresearchsql, the agents will write custom SQL statements based on the schema they discover. It is best to pair them with a supervisor agent to validate the query syntax before execution.

Q: How does CrewAI handle large tables from the Mato Grosso do Sul Open Data portal?

The researcher agent should use datastoresearch with strict limits or pagination. This prevents the agent's context window from getting overwhelmed by massive raw datasets from the state's CKAN datastore.

Deploy specialized agent crews to analyze Mato Grosso do Sul public records autonomously using CrewAI.

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Connect Mato Grosso do Sul Open Data MCP to CrewAI

Create your Vinkius account to connect Mato Grosso do Sul Open Data to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Mato Grosso do Sul Open Data with CrewAI

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Multi-agent coordination for state data analysis

Assign one CrewAI agent to map the state's structure using `list_organizations` while another queries specific tables with `datastore_search`. They share context, allowing the analyst agent to request specific resources based on what the mapper agent finds. This collaborative approach avoids redundant calls to the state's CKAN portal, making your data gathering much more efficient.

Run an autonomous SQL research desk using this MCP Server

Let a specialized researcher agent write and execute complex queries via `datastore_search_sql`. A separate editor agent can verify the output, ensuring the SQL parsed correctly and the data matches the requested package. The crew handles the entire pipeline, from discovering the dataset with `get_package` to extracting the clean records, without you writing a single SQL join.

Structured data harvesting across state groups

Your CrewAI agents use `list_groups` to segment their research by topic, such as health or education. Each agent focuses on its assigned sector, calling `get_resource` to extract the underlying CSVs or tables. The crew compiles these disparate sources into a single, cohesive report, giving you a complete look at state operations without manual searching.

Setup guide

Set up Mato Grosso do Sul Open Data MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Mato Grosso do Sul Open Data tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Mato Grosso do Sul Open Data Analyst",
    goal="Access and analyze Mato Grosso do Sul Open Data data via MCP.",
    backstory="Expert analyst with direct Mato Grosso do Sul Open Data access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Mato Grosso do Sul Open Data transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Mato Grosso do Sul Open Data Analyst",
        goal="Access and analyze Mato Grosso do Sul Open Data data via MCP.",
        backstory="Expert analyst with direct Mato Grosso do Sul Open Data access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Mato Grosso do Sul Open Data transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Mato Grosso do Sul Dados Abertos. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Mato Grosso do Sul Open Data MCP in CrewAI

You can pass the Vinkius HTTP endpoint directly into the agent's `mcps` list during initialization. CrewAI automatically discovers all tools on this MCP Server, including `datastore_search_sql` and `list_packages`.

Yes, if you give them access to `datastore_search_sql`, the agents will write custom SQL statements based on the schema they discover. It is best to pair them with a supervisor agent to validate the query syntax before execution.

The researcher agent should use `datastore_search` with strict limits or pagination. This prevents the agent's context window from getting overwhelmed by massive raw datasets from the state's CKAN datastore.

You can run it both ways, but using the Vinkius-managed HTTP transport is the simplest method. CrewAI handles the connection natively, allowing your multi-agent team to query the state's API from any environment.

No, the server acts as a direct, stateless bridge to the official government CKAN API. It does not cache or store any public records, budget sheets, or organizational structures on its own.

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