
Echomindr
- 1 repo stars
- Updated March 13, 2026
- echomindr/echomindr
EchoMindr is a MCP server that lets AI agents search founder decisions, lessons, and signals drawn from 100+ podcasts.
About
EchoMindr is a hosted Model Context Protocol server that exposes a searchable archive of founder decisions, lessons, and signals distilled from more than one hundred podcasts. developers and agent workflows can attach it via SSE to Claude Code, Cursor, or other MCP-capable clients when they need primary-style anecdotes for positioning, pricing debates, or pivot stories instead of generic blog summaries. It sits early in the journey—idea and validate—because the value is evidence and pattern-matching before implementation. The server is version 1.0.0 with a remote URL and optional self-host context via the public repository; installs and popularity metrics live in Skillselion’s ingested layer separately. Use it when your agent should cite how other founders handled distribution, fundraising tradeoffs, or product bets, then fold those insights into scope docs or landing copy. It is not a code generator or market-size database; it complements competitor and audience research by adding qualitative founder signal retrieval.
- Remote SSE MCP endpoint at echomindr.com for agent-side search
- Corpus framed as founder decisions, lessons, and signals from 100+ podcasts
- Designed for AI agents to retrieve narrative evidence, not generic web snippets
- Hosted integration (v1.0.0) with GitHub source at echomindr/echomindr
Echomindr by the numbers
- Data as of Jul 7, 2026 (Skillselion catalog sync)
claude mcp add --transport sse echomindr https://echomindr.com/mcp/Add your badge
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| repo stars | ★ 1 |
|---|---|
| Transport | SSE |
| Auth | None |
| Last updated | March 13, 2026 |
| Repository | echomindr/echomindr ↗ |
What it does
Query real founder decisions and podcast-derived lessons while shaping product direction without manually listening to hundreds of episodes.
Who is it for?
Best when you use Claude Code or Cursor and want podcast-sourced founder patterns during idea and early validation work.
Skip if: Skip if you need structured market sizing, live financial data, or ORM/database access instead of narrative founder intelligence.
What you get
Your agent can pull podcast-backed founder signals into research notes, scope conversations, and validation briefs in one MCP call chain.
- Agent-retrieved founder decision snippets for research docs
- Searchable lesson summaries tied to podcast-derived corpus
By the numbers
- 100+ podcasts in the described corpus
- MCP server version 1.0.0
- Transport: remote SSE at /mcp/
README.md
Echomindr
3,500+ real founder moments from 60+ podcasts — searchable by AI agents.
Each moment: a named founder, a verbatim quote, a decision taken, an outcome observed, a lesson extracted — with a timestamped link to the source. Not summaries. Not paraphrases. What actually happened.
Why
AI agents give generic startup advice. Echomindr gives them access to what founders actually did.
Ask: "How did founders handle their first pricing?" Get: Kevin Hale's 10-5-20 rule, Josh Pigford charging $249/month from day one, Madhavan Ramanujam's options trick — with quotes, outcomes, and source links.
Ask: "What did founders do when they nearly ran out of money?" Get: Airbnb selling cereal boxes, Notion's near-collapse during COVID, Calm's years of slow growth before the breakout — directly from the founders who lived it.
Quick start
API (REST)
# Search for founder experiences
curl "https://echomindr.com/search?q=pricing&limit=5"
# Describe a situation, get matching experiences (vector search)
curl -X POST "https://echomindr.com/situation" \
-H "Content-Type: application/json" \
-d '{"situation": "B2B SaaS founder with free pilots that won'\''t convert to paid"}'
# Get moment details
curl "https://echomindr.com/moments/{id}"
# Find similar moments
curl "https://echomindr.com/similar/{id}?limit=5"
API docs: echomindr.com/docs
MCP (for AI agents)
Connect via remote MCP: https://echomindr.com/mcp/
Or add to Claude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"echomindr": {
"command": "python",
"args": ["echomindr_mcp.py"],
"env": {
"ECHOMINDR_API_URL": "https://echomindr.com"
}
}
}
}
3 MCP tools:
search_experience— semantic search for founder stories by situationget_experience_detail— full details of a moment (quote, decision, outcome, lesson)find_similar_experiences— related founder stories by shared themes
llms.txt
https://echomindr.com/llms.txt
Data
- 3,500+ moments from 340+ podcast episodes across 60+ shows
- 52 canonical situations across 10 thematic families (PMF, growth, pricing, fundraising, team, operations, resilience, strategy, founder psychology, hostile environments)
- 5 moment types: decision, problem, lesson, signal, advice
- 5 stages: idea, mvp, traction, scale, mature
- Sources: How I Built This, Lenny's Podcast, 20 Minute VC, Acquired, Y Combinator, My First Million, GDIY (Génération Do It Yourself), Disrupting Japan, Silicon Carne, Startup Ministerio, Kevin Kamis, Wall Street Paper, Valy Sy (China), Matt & Ari (Canada), Oscar Lindhardt (Denmark), Aidan Walsh (USA)
Each moment: summary · verbatim quote · decision · outcome · lesson · stage · tags · timestamp link
Self-hosting
To run your own instance with the sample data:
git clone https://github.com/echomindr/echomindr.git
cd echomindr
pip install -r requirements.txt
# Build a sample database
python echomindr_build_db.py --sample
# Start the API
python echomindr_api.py
# → http://localhost:8000/docs
To build the full database, you need your own podcast transcriptions and Claude API key. See echomindr_extract_v2.py for the extraction pipeline.
Architecture
Podcast audio → Deepgram (transcription) → Claude (extraction) → SQLite → FastAPI → MCP
The extraction pipeline turns long-form podcast interviews into structured, searchable moments. Each episode yields 8–15 moments on average. Semantic search uses BAAI/BGE-M3 embeddings (1024-dim) via sqlite-vec.
Endpoints
| Endpoint | Method | Description |
|---|---|---|
/search |
GET | Full-text search with stage/type filters |
/situation |
POST | Describe a situation, get matching experiences (vector search) |
/moments/{id} |
GET | Full moment detail |
/similar/{id} |
GET | Similar moments by shared tags |
/taxonomy |
GET | 52 canonical situations across 10 families |
/stats |
GET | Database statistics |
/llms.txt |
GET | LLM-optimized API description |
/docs |
GET | Swagger documentation |
License
MIT — the code is open source. The hosted database at echomindr.com is a managed service.
Built by Thierry — author of "The System That Learns Wins" and "Designing for Permanent Hostility".
Recommended MCP Servers
How it compares
Hosted research MCP over podcast-derived founder signals—not a Postgres connector or a generic web search skill.
FAQ
Who is EchoMindr for?
Developers who use AI coding agents and want searchable founder lessons from 100+ podcasts during research and scoping.
When should I use EchoMindr?
Use it when brainstorming positioning, pricing, or pivots and you want the agent to ground answers in documented founder decisions rather than hallucinated anecdotes.
How do I add EchoMindr to my agent?
Register the remote SSE MCP server URL https://echomindr.com/mcp/ in your client’s MCP config (Claude Code, Cursor, or other SSE-capable hosts), then invoke search tools from the agent session.