OPML Podcast & RSS Parser MCP Server for LlamaIndexGive LlamaIndex instant access to 1 tools to Parse Opml Feeds

Name: OPML Podcast & RSS Parser
Price: 29 USD
Availability: InStock
Rating: 4.7 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add OPML Podcast & RSS Parser as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Ask AI about this MCP Server for LlamaIndex

Open in ChatGPT Open in Claude Open in Perplexity

The OPML Podcast & RSS Parser MCP Server for LlamaIndex is a standout in the Productivity category — giving your AI agent 1 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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ChatGPT

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VS Code

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+ other MCP clients

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to OPML Podcast & RSS Parser. "
            "You have 1 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in OPML Podcast & RSS Parser?"
    )
    print(response)

asyncio.run(main())

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About OPML Podcast & RSS Parser MCP Server

When you want Claude to recommend new podcasts or blogs based on what you already consume, you export an .opml file from Apple Podcasts, Pocket Casts, or Feedly. But XML outlines are noisy, recursive, and confusing for LLMs to read efficiently.

LlamaIndex agents combine OPML Podcast & RSS Parser tool responses with indexed documents for comprehensive, grounded answers. Connect 1 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

This MCP uses a fast, deterministic XML parser to flatten the OPML hierarchy into a simple, structured list of your subscriptions, dropping all the unnecessary XML tags and attributes.

The Superpowers

Universal Support: Parses OPML files from any standard podcast player or RSS reader.
Zero Token Waste: Converts heavy XML markup into a clean, flat JSON array.
Local Privacy: Your subscription habits are parsed locally, ensuring they aren't uploaded to a public server.
Assistant Ready: Turn Claude into your personal entertainment and news curator.

The OPML Podcast & RSS Parser MCP Server exposes 1 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 1 OPML Podcast & RSS Parser tools available for LlamaIndex

When LlamaIndex connects to OPML Podcast & RSS Parser through Vinkius, your AI agent gets direct access to every tool listed below — spanning rss, podcast-management, xml-parsing, 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.

parse

Parse opml feeds on OPML Podcast & RSS Parser

Provide the absolute file path. Parse an OPML file (Podcast or RSS feed export) into a clean JSON list of subscriptions

Connect OPML Podcast & RSS Parser to LlamaIndex via MCP

Follow these steps to wire OPML Podcast & RSS Parser into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 1 tools from OPML Podcast & RSS Parser

Why Use LlamaIndex with the OPML Podcast & RSS Parser MCP Server

LlamaIndex provides unique advantages when paired with OPML Podcast & RSS Parser through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine OPML Podcast & RSS Parser tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain OPML Podcast & RSS Parser tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query OPML Podcast & RSS Parser, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what OPML Podcast & RSS Parser tools were called, what data was returned, and how it influenced the final answer

OPML Podcast & RSS Parser + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the OPML Podcast & RSS Parser MCP Server delivers measurable value.

Hybrid search: combine OPML Podcast & RSS Parser real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query OPML Podcast & RSS Parser to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying OPML Podcast & RSS Parser for fresh data

Analytical workflows: chain OPML Podcast & RSS Parser queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for OPML Podcast & RSS Parser in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with OPML Podcast & RSS Parser immediately.

"Read my podcasts.opml file and recommend 3 new tech podcasts I might like."

"Extract all the RSS URLs from my Feedly export and format them as a Markdown table."

"Analyze my OPML file and tell me what my primary interests are."

Troubleshooting OPML Podcast & RSS Parser MCP Server with LlamaIndex

Common issues when connecting OPML Podcast & RSS Parser to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

OPML Podcast & RSS Parser + LlamaIndex FAQ

Common questions about integrating OPML Podcast & RSS Parser MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query OPML Podcast & RSS Parser tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.