Audioscrape MCP
Audioscrape MCP lets agents search and retrieve speaker-attributed audio, transcripts, entities, and citations from public and workspace content.
Verdict
Common use cases
- Draft podcast show notes from episodes
- Transcribe customer support call recordings
- Extract quotes from video interviews
- Search spoken content across team meetings
- Generate summaries of webinar audio
Integration
- Vendor
- Audioscrape MCP
- Category
- other
- Auth
- OAUTH2
- Composio slug
audioscrape_mcp
Tools
Setup
Setup guide
- 11. Open your Switchy workspace and navigate to Settings > Integrations. 2. Find Audioscrape MCP in the catalog and click Connect. 3. You'll be redirected to authorize Audioscrape to access audio files on your behalf—this typically includes read permissions for cloud storage or media libraries. 4. Review the requested scopes (file access, transcription services) and click Authorize. 5. Switchy will confirm the connection and return you to the integrations page. 6. Open any Project and type '@Audioscrape' followed by a request like 'transcribe the latest podcast episode'—if the MCP responds with text output, your connection is live. 7. If authentication fails, revisit Settings > Integrations to reconnect and verify you granted all required permissions.
What teammates see: by default, memories from Audioscrape MCP are scoped to the Project (PROJECT visibility) - you can mark any memory PRIVATE or share it ORG-wide.
Works well with
Top models
Compatibility data appears once enough Projects have used this MCP together with a given model.
How Switchy teams use it
Starter prompts
Transcribe Podcast Episode
@Audioscrape transcribe the audio from this podcast episode URL and format the output as a clean transcript with speaker labels.
Extract Interview Quotes
@Audioscrape listen to this interview recording and pull out the five most quotable statements with timestamps.
Summarize Meeting Audio
@Audioscrape transcribe this meeting audio file and generate a summary highlighting decisions and next steps.
Search Call Recordings
@Audioscrape search these customer call recordings for mentions of 'billing error' and return relevant excerpts.
Generate Video Captions
@Audioscrape pull audio from this video file and create a timestamped caption file in SRT format.
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
@audioscrape extract the transcript and speaker labels from the podcast episode at https://example.com/episode-42.mp3
Extracted transcript with 3 speakers identified across 47 minutes of audio. Speaker A (likely host) spoke for 28 minutes, Speaker B for 14 minutes, Speaker C for 5 minutes. Transcript includes timestamped segments and confidence scores. Full text available for search or summarization. Note: Speaker identification is algorithmic and may require manual verification for accuracy.
This example shows audio-to-text extraction with diarization (speaker separation). OAuth2 scope must include read access to process external URLs. Processing time scales with audio length — expect 2-5 minutes for hour-long files. Confidence scores help identify segments needing human review.
@audioscrape analyze the sentiment and key topics discussed in the last team standup recording I uploaded yesterday
Analyzed 18-minute standup recording. Overall sentiment: neutral-to-positive (0.62 score). Key topics detected: deployment blockers (mentioned 7 times, negative sentiment), Q4 roadmap alignment (5 mentions, positive), resource allocation concerns (3 mentions, mixed). Two action items identified from speech patterns. Transcript shows 6 participants with relatively balanced speaking time.
This demonstrates content analysis beyond transcription — sentiment scoring and topic extraction from meeting audio. Requires prior upload or storage integration. Results depend on audio quality and clear speech. Topic detection works best with structured conversations like meetings rather than freeform podcasts.
@audioscrape compare the terminology and speaking pace between our sales call from Monday and the competitor demo recording, then draft talking points for our next pitch
Comparison complete. Your Monday call: 145 words/minute average pace, 12 product-specific terms used, 8-minute feature discussion. Competitor demo: 128 words/minute, 19 technical terms, 14-minute feature walkthrough with more pauses for questions. Competitor emphasized integration capabilities (mentioned 11 times vs. your 3). Draft talking points: slow pace slightly, expand integration narrative, add 4-5 strategic pauses for engagement. Competitor used 'seamless' 6 times — consider alternative framing.
This synthesis example pairs audio analysis with AI reasoning to generate strategic insights. Requires access to multiple audio sources and may hit rate limits on bulk processing. Comparative analysis accuracy depends on similar recording conditions. Always verify competitive intelligence claims before using in customer conversations.
Use-case deep-dives
When audio transcription fits a weekly content sprint
A 3-person content team publishes a weekly newsletter that curates insights from industry podcasts. They need transcripts of 8-12 episodes per week to pull quotes and verify claims. Audioscrape MCP works here because OAuth2 auth means the team can share one integration without passing API keys around, and the MCP handles the fetch-and-transcribe loop in a single prompt. The threshold: if your team is processing more than 20 hours of audio per week, you'll want a dedicated transcription service with speaker diarization and custom vocabulary. Below that, this MCP keeps the workflow inside Switchy without adding another SaaS login.
Turning recorded calls into insight docs for product teams
A 5-person product team records 4-6 customer interviews per sprint and needs summaries in a shared doc before the retro. Audioscrape MCP fits if your calls are under 90 minutes and you're okay with a single-pass transcription (no custom speaker labels). The OAuth2 flow means any team member can kick off the transcription from Switchy without needing admin access to a separate tool. The trade-off: if you need to tag speakers by name or export timestamped clips, you'll outgrow this fast. For early-stage teams doing lightweight synthesis, it closes the loop between recording and analysis without adding another seat license.
Making onboarding recordings searchable for remote teams
A 10-person remote team has 30+ onboarding videos recorded over Zoom, and new hires can't find the right clip when they need it. Audioscrape MCP solves this if you're willing to batch-process the backlog once and then handle new recordings as they come in. The MCP pulls audio, transcribes it, and drops the text into Switchy where the team can search across all transcripts in one workspace. The limit: if your video library is over 100 hours or you need real-time captioning during live calls, this isn't the tool. For teams with a static or slow-growing library, it turns a pile of recordings into a searchable knowledge base without paying for a video platform upgrade.
Frequently asked
What does Audioscrape MCP do in Switchy?
Audioscrape MCP connects audio scraping capabilities to your Switchy workspace, letting AI agents extract and process audio content from various sources. Since the integration uses OAuth2, your team authenticates once and agents can request audio data without manual downloads. It's useful for teams building voice analysis workflows or transcription pipelines inside Switchy.
Do I need special permissions to connect Audioscrape MCP?
You'll need an Audioscrape account with API access enabled. The OAuth2 flow requests read permissions for audio sources you've configured in Audioscrape. Most teams connect this with a dedicated service account rather than a personal login, so access doesn't break when someone leaves. Check your Audioscrape plan to confirm API access is included.
Can Audioscrape MCP process live audio streams?
This depends on what Audioscrape's API supports. Most audio scraping tools focus on recorded content rather than real-time streams. If you need live transcription or analysis, you're better off using a dedicated speech-to-text MCP like Deepgram or AssemblyAI. Audioscrape works best for batch processing of existing audio files.
How is this different from uploading audio files directly to Switchy?
Audioscrape MCP pulls audio from external sources automatically, so you don't manually download and re-upload files. If your audio lives in podcasts, video platforms, or content feeds, the MCP fetches it on demand. For one-off files you already have locally, direct upload is faster. Use Audioscrape when you're building repeatable workflows that pull from the same sources.
Who on the team should set up Audioscrape MCP?
Whoever manages your Audioscrape account and understands which audio sources your workflows need. This person should connect the integration in Switchy's settings, then share access with teammates who build agents. The OAuth token is workspace-wide, so you only authenticate once. Audio processing doesn't count against Switchy's message limits, but check Audioscrape's API quotas.