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Podcast Index MCP Server for LangChainGive LangChain instant access to 16 tools to Get Episode By Guid, Get Episodes By Feed Id, Get Episodes By Feed Url, and more

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LangChain is the leading Python framework for composable LLM applications. Connect Podcast Index through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Ask AI about this MCP Server for LangChain

The Podcast Index MCP Server for LangChain is a standout in the Audio Music category — giving your AI agent 16 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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python
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "podcast-index": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Podcast Index, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())
Podcast Index
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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

About Podcast Index MCP Server

Connect to the Podcast Index to tap into a massive, independent database of podcasts and episodes. This MCP server allows your AI agent to browse the open podcasting directory without the restrictions of proprietary platforms.

LangChain's ecosystem of 500+ components combines seamlessly with Podcast Index through native MCP adapters. Connect 16 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

  • Deep Search — Find podcasts by general terms, specific titles, or even by the people (hosts/guests) featured in them.
  • Metadata Retrieval — Fetch comprehensive details using RSS feed URLs, Index IDs, Podcast GUIDs, or iTunes IDs.
  • Episode Discovery — List all episodes for a specific feed or find individual episodes by their unique GUID.
  • Trending & Recent — Stay updated with recent feeds and episodes, or discover something new with random episode selection.

The Podcast Index MCP Server exposes 16 tools through the Vinkius. Connect it to LangChain in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 16 Podcast Index tools available for LangChain

When LangChain connects to Podcast Index through Vinkius, your AI agent gets direct access to every tool listed below — spanning podcasting, directory-api, metadata-retrieval, 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 episode by guid on Podcast Index

Get a specific episode by its GUID

get

Get episodes by feed id on Podcast Index

List episodes for a specific feed ID

get

Get episodes by feed url on Podcast Index

List episodes for a specific feed URL

get

Get podcast by feed id on Podcast Index

Get podcast details using its internal Index ID

get

Get podcast by feed url on Podcast Index

Get podcast details using its RSS feed URL

get

Get podcast by guid on Podcast Index

Get podcast details using its Podcast GUID

get

Get podcast by itunes id on Podcast Index

Get podcast details using its iTunes ID

get

Get random episodes on Podcast Index

Get a selection of random episodes

get

Get recent episodes on Podcast Index

Get the most recently published episodes

get

Get recent feeds on Podcast Index

Get the most recently added or updated feeds

get

Get recent new feeds on Podcast Index

Get feeds newly added to the index

get

Get value by feed id on Podcast Index

Get the value block by feed ID

get

Get value by feed url on Podcast Index

Get the value block (e.g., Lightning Network details) for a feed URL

search

Search by person on Podcast Index

Search for podcasts featuring a specific person

search

Search by term on Podcast Index

Search for podcasts by a general search term

search

Search by title on Podcast Index

Search for podcasts by title

Connect Podcast Index to LangChain via MCP

Follow these steps to wire Podcast Index into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

01

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save the code and run python agent.py
04

Explore tools

The agent discovers 16 tools from Podcast Index via MCP

Why Use LangChain with the Podcast Index MCP Server

LangChain provides unique advantages when paired with Podcast Index through the Model Context Protocol.

01

The largest ecosystem of integrations, chains, and agents. combine Podcast Index MCP tools with 500+ LangChain components

02

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

03

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

04

Memory and conversation persistence let agents maintain context across Podcast Index queries for multi-turn workflows

Podcast Index + LangChain Use Cases

Practical scenarios where LangChain combined with the Podcast Index MCP Server delivers measurable value.

01

RAG with live data: combine Podcast Index tool results with vector store retrievals for answers grounded in both real-time and historical data

02

Autonomous research agents: LangChain agents query Podcast Index, synthesize findings, and generate comprehensive research reports

03

Multi-tool orchestration: chain Podcast Index tools with web scrapers, databases, and calculators in a single agent run

04

Production monitoring: use LangSmith to trace every Podcast Index tool call, measure latency, and optimize your agent's performance

Example Prompts for Podcast Index in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Podcast Index immediately.

01

"Search for podcasts about artificial intelligence using a general term."

02

"Find all podcasts featuring Lex Fridman."

03

"Get the latest episodes for the podcast with feed ID 750746."

Troubleshooting Podcast Index MCP Server with LangChain

Common issues when connecting Podcast Index to LangChain through Vinkius, and how to resolve them.

01

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Podcast Index + LangChain FAQ

Common questions about integrating Podcast Index MCP Server with LangChain.

01

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.
02

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.
03

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.

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