Bring Rss
to CrewAI
Learn how to connect OPML Podcast & RSS Parser to CrewAI and start using 1 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the 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.
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.
Built-in capabilities (1)
Provide the absolute file path. Parse an OPML file (Podcast or RSS feed export) into a clean JSON list of subscriptions
Why CrewAI?
When paired with CrewAI, OPML Podcast & RSS Parser becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call OPML Podcast & RSS Parser tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
- —
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
mcpsparameter 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
OPML Podcast & RSS Parser in CrewAI
OPML Podcast & RSS Parser and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect OPML Podcast & RSS Parser to CrewAI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for OPML Podcast & RSS Parser in CrewAI
The OPML Podcast & RSS Parser MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 1 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in CrewAI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
OPML Podcast & RSS Parser for CrewAI
Every tool call from CrewAI to the OPML Podcast & RSS Parser MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Does it support nested subscription categories?
Yes! It recursively scans through folder nodes (like 'Tech News' -> 'AI') in the OPML file to extract the actual feed URLs, flattening them into a clean list for the AI.
What specific data is extracted?
It extracts the Title, the XML (RSS) URL, and the HTML (Website) URL for every single subscription found in the file.
Can it subscribe to new podcasts for me?
No, this is a read-only parsing tool. It allows the AI to understand what you currently listen to so it can make intelligent recommendations.
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.
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.
Explore More MCP Servers
View all →
Inep Dados Abertos
12 toolsAccess official Brazilian educational data from INEP — query Censo Escolar, ENEM, and higher education statistics directly.
Slack
6 toolsAutomate Slack messaging — send messages, search conversations, list channels and users directly from any AI agent.
Blink Payment
10 toolsManage payments, customers, and paylinks seamlessly using Blink Payment with AI Agents.
OFX Bank Statement Parser
1 toolsTurn archaic OFX/QFX bank exports into clean JSON transactions safely and local. Let your AI act as your personal accountant without uploading sensitive financial data.