mcpField
P
Pod Researcher

Pod Researcher

Remote
pipeworx.ioBusiness & Marketing0Updated 5w ago

About Pod Researcher

Pod Researcher helps podcast guest bookers and sponsors find active shows matching a topic, with published contact routes, episode evidence, and outreach tracking. Searches use recent episode titles and notes.

Key features

  1. 01Topic-ranked active podcasts
  2. 02Published contact routes by purpose
  3. 03Episode evidence for each match
  4. 04Podcast profiles and feed refresh
  5. 05Topic watches and change alerts
  6. 06Per-account outreach history

Use cases

  1. 01Find shows for a guest pitch
  2. 02Identify podcasts for sponsorship
  3. 03Check episodes supporting a topic match
  4. 04Avoid repeating declined or completed outreach

MCP support

Exposes podcast search, profile, refresh, topic-watch, and outreach tools to MCP clients. It can run as a gateway pack or a local stdio MCP server.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://gateway.pipeworx.io/pod-researcher/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

Details

From the project README

@pipeworx/pod-researcher

Podcast prospecting for guest booking and sponsorship: given a topic and a purpose, returns active podcasts whose recent episodes cover the topic, each with the best contact route the show itself published, the episodes that prove the fit, and your own outreach history so you never pitch the same show twice.

Part of Pipeworx — an MCP gateway connecting AI agents to 1684+ live data sources.

Tools

  • find_podcast_contacts(topic, purpose, require_route?, language?, country?, active_only?, include_engaged?, limit?, cursor?) — active shows ranked by topical fit, each with best_route (or route_status: "none_found" plus what was checked), evidence episodes, coverage counts and data_as_of. purpose is guest or sponsor; routes designated for that purpose rank first, and a general contact or a person's published address is a labelled fallback. require_route: true returns only shows that have one.
  • get_podcast_profile(id) — one show in full: identity, activity status, feed health, latest episodes, every published route. id is a show_id, an RSS feed URL (moved feeds resolve by their old URL) or a podcast:guid.
  • refresh_podcast(id) — re-read one show's RSS feed now rather than waiting for its next scheduled check.
  • watch_podcast_topic(topic, purpose, ...) / get_podcast_topic_changes(watch_id?) — save a search and get the shows that newly match it.
  • record_podcast_outreach(show_id, outcome, scope?, contact_id?) — record contacted / replied / declined / booked / bounced / do_not_contact / clear. Declined and do_not_contact shows leave your searches; a scope: "person" record hides every show that publishes that person's address. Private to your account.

Auth

Keyless to search. watch_podcast_topic, get_podcast_topic_changes and record_podcast_outreach need a signed-in account (an API key from pipeworx.io/signup): outreach history is kept per account, and an anonymous caller has none. This pack runs only on the Pipeworx gateway; a standalone build refuses every call rather than returning an empty list.

Contacts: what is returned and how it is labelled

Every address a show publishes in its own RSS feed or on its own website is returned, labelled:

  • holder_type: role (a function inbox such as booking@ or sponsorships@), hosting_account_owner (the itunes:owner address from the feed, usually the host), or person (an individual's own address).
  • purpose (guest, sponsor, general) and confidence (designated when the show invited that purpose in words, fallback otherwise), plus match on the best route: designated_for_purpose or general_fallback.
  • source_url (the page or feed it was read from) and last_observed.

Addresses are never synthesised. A global removal list is honoured at serve time. record_podcast_outreach never takes an address: a call carrying anything email-shaped is refused without repeating it, and a person-level record stores a SHA-256 of the address, not the address.

Data sources

  • Podcast Index open directory (https://podcastindex.org) — which podcasts exist, their feed URLs and identifiers.
  • Each show's RSS feed — episodes, publishing cadence, the itunes:owner address and invitation lines in episode notes.
  • Each show's own website — its guest, advertise and contact pages.

Matching is on words in episode titles and show notes (the latest ~30 episodes per show), so a show that never names the topic in its notes is not found. Generated-content spam is left out by a classifier that also catches some real shows whose episode notes disclose AI assistance. Contact coverage is still growing: route_check on each result says whether the show's website has been read yet, so "none found" and "not looked at yet" are distinguishable.

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "pod-researcher": {
      "url": "https://gateway.pipeworx.io/pod-researcher/mcp"
    }
  }
}
What this endpoint actually serves

tools/list at https://gateway.pipeworx.io/pod-researcher/mcp returns the tools in the table above plus the shared Pipeworx meta-tools — ask_pipeworx, discover_tools, search_within, remember/recall and the rest of the gateway-wide set. So the tool count you see is larger than this table: a single-pack endpoint currently lists roughly 30 shared tools alongside the pack's own. The connection's initialize response states its exact scope, and is the authoritative answer for a given day.

This is deliberate, not multiplexing by accident. The meta-tools are what let a scoped connection answer a question this pack does not cover — via ask_pipeworx, which routes across the whole catalog — without you adding a second MCP server. There is currently no way to mount a pack endpoint without them; if the extra schemas cost you more context than the routing is worth, connect to the full gateway once rather than to several pack endpoints.

Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:

{
  "mcpServers": {
    "pipeworx": {
      "url": "https://gateway.pipeworx.io/mcp"
    }
  }
}

Both URLs reach the same gateway and the same 1684+ data sources. The only difference is which pack's tools are listed directly; ask_pipeworx reaches all of them from either one.

No MCP client? Call it over HTTP

curl -X POST https://gateway.pipeworx.io/v1/tools/find_podcast_contacts \
  -H 'Content-Type: application/json' \
  -d '{"topic":"AI agents developer infrastructure","purpose":"guest","limit":5}'

No account needed for the first calls. Inspect any tool: GET https://gateway.pipeworx.io/v1/tools/find_podcast_contacts. Find one: POST https://gateway.pipeworx.io/v1/tools/search_packs with {"query":"..."}.

Standalone (no gateway account)

This package also runs as a local stdio MCP server — no Pipeworx account, no gateway round-trip:

{
  "mcpServers": {
    "pod-researcher": {
      "command": "npx",
      "args": ["-y", "@pipeworx/mcp-pod-researcher"]
    }
  }
}

Or run it directly to confirm it starts:

npx -y @pipeworx/mcp-pod-researcher

It speaks MCP over stdin/stdout and answers initialize/tools/list/tools/call for only this pack's tools — none of the shared meta-tools the gateway connection above adds. Same source, same tools, no ask_pipeworx routing.

Using with ask_pipeworx

Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:

ask_pipeworx({ question: "your question about Pod Researcher data" })

The gateway picks the right tool and fills the arguments automatically.

More

License

MIT

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.