How to Use the Comunidad de Madrid (Portal Regional) MCP in LlamaIndex

Q: How do I index Comunidad de Madrid (Portal Regional) metadata into a LlamaIndex vector store?

Run getdataset to retrieve the metadata, then wrap the JSON output in a LlamaIndex Document object. Pass this document to your VectorStoreIndex to make the Madrid metadata searchable.

Q: Can I run hybrid search on Comunidad de Madrid (Portal Regional) datasets using LlamaIndex?

Yes, you can combine keyword search using searchdatasets with vector search over your indexed documents. This helps you locate specific Madrid records using both exact terms and semantic meaning in your LlamaIndex pipeline.

Q: How does the Comunidad de Madrid (Portal Regional) MCP server prevent hallucinations in LlamaIndex?

It feeds real-time data from searchdatastore directly into your agent's context window. Grounding your LlamaIndex LLM in actual Madrid registry rows ensures its answers are accurate.

Q: How do I filter Madrid resources before indexing them in LlamaIndex?

Use getresource to check the file format and size of the Madrid dataset. Your LlamaIndex ingest pipeline can then skip non-text resources or oversized files before indexing.

Index raw public records from Comunidad de Madrid (Portal Regional) into your LlamaIndex vector stores.

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Connect Comunidad de Madrid (Portal Regional) MCP to LlamaIndex

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Setup Comunidad de Madrid (Portal Regional) with LlamaIndex

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Index Madrid datasets for semantic search.

`get_dataset` fetches full metadata for any regional dataset you target. LlamaIndex ingests this structured payload, turning it into document nodes that populate your local vector index. This lets your agent query the index later instead of hitting the live Madrid API repeatedly. You get instant answers about Madrid's public administration structure grounded in actual portal metadata.

Build RAG pipelines with the Madrid MCP Server.

`search_datastore` queries live tabular data directly from Madrid's public database. This LlamaIndex MCP server integration queries raw rows, converts them into text chunks, and indexes them on the fly. Real-time workflows like this prevent hallucinations when users ask about regional Madrid statistics. Your LlamaIndex agent answers questions using the freshly indexed datastore records rather than relying on stale training data.

Search and store regional resources.

`search_datasets` scans the Madrid catalog for specific topics like health or transit. The tool returns a list of resources that your LlamaIndex pipeline immediately processes into queryable indices. Once loaded, you can use `get_resource` to extract Madrid file metadata for specific formats. This ensures your LlamaIndex index only contains active, compatible data sources from the regional portal.

Setup guide

Set up Comunidad de Madrid (Portal Regional) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Comunidad de Madrid (Portal Regional) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Comunidad de Madrid (Portal Regional) tools.",
)
response = await agent.run("List recent Comunidad de Madrid (Portal Regional) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Comunidad de Madrid (Portal Regional). 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 Comunidad de Madrid (Portal Regional) MCP in LlamaIndex

Run `get_dataset` to retrieve the metadata, then wrap the JSON output in a LlamaIndex Document object. Pass this document to your VectorStoreIndex to make the Madrid metadata searchable.

Yes, you can combine keyword search using `search_datasets` with vector search over your indexed documents. This helps you locate specific Madrid records using both exact terms and semantic meaning in your LlamaIndex pipeline.

It feeds real-time data from `search_datastore` directly into your agent's context window. Grounding your LlamaIndex LLM in actual Madrid registry rows ensures its answers are accurate.

Use `get_resource` to check the file format and size of the Madrid dataset. Your LlamaIndex ingest pipeline can then skip non-text resources or oversized files before indexing.

No, your queries are processed locally within your LlamaIndex application. The MCP server only forwards the specific API parameters required by `search_datastore` to fetch the public Madrid data, keeping your internal search history private.

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