Podigee Analytics MCP Server

Podigee MCP Server: Conversational Podcast Analytics

The Podigee MCP Server enables conversational podcast analytics.

Connect an MCP-compatible AI client to your Podigee analytics and workflows, then ask questions in natural language (for example, “What were my top episodes this month?”) to get instant, actionable insights you can use for reporting, editorial decisions, and growth.

Summary

What it is: An MCP (Model Context Protocol) server that securely connects AI clients to your Podigee podcast data and operations.
What it replaces: Manual dashboard digging and repetitive reporting workflows.
What you get: Fast answers, comparisons, trends, and report-ready summaries using plain language.
Best for: Podcasters, networks, media teams, agencies, developers, and BI analysts who need flexible analytics and custom reporting.
Access: Requires a Podigee account, an API key, and repository access for the MCP Server (available on request).

TLDR; for devs

Find the MCP server code here: https://github.com/podigee/mcp-server

What is MCP (Model Context Protocol)?

MCP (Model Context Protocol) is an open protocol that standardizes how AI applications connect to external tools and data sources. In MCP terms, an AI application acts as a host and uses an MCP client to connect to an MCP server, which exposes capabilities (tools, resources) in a consistent way.

In practice, MCP works like a “universal connector” for AI: instead of building a custom integration for every model and every dataset, you connect once through a standard protocol and reuse that connection across compatible clients.

What the Podigee MCP Server provides

The Podigee MCP Server turns podcast analytics and workflows into an interface that an AI client can query and (where enabled) act on.

Core analytics capabilities

Natural language queries: Ask questions like “How many downloads did we get this week?” and get a direct answer.
Episode comparisons: Identify top episodes, outliers, and repeatable patterns.
Trend analysis: Compare time ranges (week-over-week, quarter-over-quarter) and summarize what changed.
Network-level views: Analyze one podcast or aggregate across multiple shows.
Report-ready output: Generate summaries suitable for internal updates, sponsor decks, or performance reviews.

Beyond analytics (workflow and content support)

Workflow automation: Reduce repetitive operational tasks by routing them through a tool-driven interface.
Content acceleration: Generate structured summaries and draft assets like show notes, episode recaps, or social copy (based on your analytics and metadata).
Monetization support: Identify high-performing episodes and themes that are strong candidates for sponsor pitches or premium content ideas.

Common use cases

Creators and producers: Weekly performance reviews, topic decisions, episode retrospectives, and growth experiments.
Podcast networks: Cross-show reporting, benchmarking, and scalable monthly executive summaries.
Agencies and studios: Client reporting and “what worked, what did not” analysis across multiple podcasts.
Developers and BI teams: Custom dashboards, scheduled reports, and deep exploratory analysis without building one-off queries for every question.

Example questions (prompt library)

Performance and reporting

“What were my top episodes last month by downloads? Include rank, title, and the key reason each performed well.”
“Summarize this month’s performance in 6 bullet points for an executive update.”
“Compare this quarter vs last quarter and explain the biggest drivers of change.”

Content strategy

“Which topics consistently outperform our median episode?”
“Compare interviews vs solo episodes. Which format performs better and why?”
“Which episode titles correlate with higher performance? Give examples.”

Growth and monetization

“Which episodes are best candidates for sponsor outreach? Provide a short pitch angle per episode.”
“Which themes should we double down on next month based on the last 90 days?”

How it works (high level)

Your AI client (an MCP-compatible application) connects to the Podigee MCP Server.
The MCP Server authenticates and retrieves the relevant Podigee data (based on your permissions and configuration).
The AI client presents answers and structured outputs you can iterate on with follow-up questions.

Getting started

Prerequisites

A Podigee account
A Podigee API key (from your account settings)
Access to the Podigee MCP Server repository (request access from Podigee)
An MCP-compatible AI client (or your own client implementation)

Setup steps

Get your Podigee API key. Store it securely (server-side). Do not embed it in client-side code.
Request repository access. Contact Podigee to get access to the MCP Server repo and onboarding instructions.
Run the MCP Server in a secure environment. Use a trusted machine or private infrastructure. Avoid public exposure unless you have appropriate authentication and network controls.
Connect your MCP client. Configure your AI client to talk to the Podigee MCP Server endpoint.
Verify the connection. Start with a simple query like “List my podcasts” or “Top episodes in the last 30 days”.

Where to find the demo

Podigee’s launch article includes a walkthrough and an introduction video:

Unlock the Future of Podcast Analytics with Podigee’s MCP Server

Security and operational guidance

The Podigee MCP Server should be treated like integration infrastructure.

Do not expose it directly to listeners or untrusted public traffic.
Keep secrets server-side: Store API keys and tokens securely and rotate them if exposed.
Use access controls: Limit who can connect, and limit which actions are enabled.
Plan for rate limits: Analytics queries can trigger multiple underlying requests. Cache and batch where appropriate.
Log responsibly: Avoid storing sensitive data in logs unless you explicitly need it.

Troubleshooting

No data returned: Expand the time range, confirm the selected podcast, and verify permissions.
Authentication errors: Re-check API key configuration and confirm that your account has the required access.
Rate limiting: Reduce query frequency, add caching, and avoid repeated refresh loops.
Ambiguous answers: Make the query more specific (time window, podcast name, metric, and desired format).

FAQ

Is the Podigee MCP Server a replacement for analytics dashboards?

No. It complements dashboards by making ad hoc questions, cross-show reporting, and custom summaries faster and more flexible.

Do I need to be a developer to use it?

Basic technical comfort helps (API keys, configuration), but the primary interface is natural language. If you can ask a precise question, you can get value quickly.

Which AI clients can connect?

Any client that supports MCP can connect. MCP is designed as a standard protocol, so compatibility depends on the AI client or tooling you use.

Learn more (authoritative references)

Podigee’s vision: “The MCP server is our first step toward a wave of AI-driven tools that make publishing smarter and more intuitive. It frees podcasters from rigid analytics and lets them explore their data in ways that matter to them.”