A Model Context Protocol (MCP) server that transforms The Build Podcast into a searchable knowledge base with thousands of AI insights using advanced hybrid search. Combines vector semantic similarity with full-text search to help you discover business ideas, frameworks, and product strategies. Access the collective wisdom of builders and entrepreneurs through natural language queries, making podcast knowledge instantly actionable.

Our MCP Server sources it's information from The Build Vault. The Build Vault is an intelligent archive of AI-focused insights, products, ideas and news extracted from The Build Podcast episodes. The backend powers a sophisticated AI-driven data processing pipeline that consists of the following stages:

Core Processing Pipeline

• YouTube Episode Extraction and Audio Download
• AssemblyAI Transcriptions with speaker diarization, sentiment analysis, and auto highlights
• Segment Processing with AI-enhanced titles, topics, and key phrases

LLM Driven Content Extraction

• 150-250 word summaries
• Extract insights across Frameworks & Exercises, Points of View, Business Ideas, Stories & Anecdotes, Quotes, and Products
• Product Extraction: Automatically identifies and tracks product mentions from insights, preparing them for enrichment workflows
• Link Processing: Extracts URLs from YouTube descriptions and enriches them with AI-powered summaries, categorization, and key takeaways

Advanced Search & Discovery

• Vector Embeddings: Generates embeddings for semantic search capabilities
• Hybrid Search: Combines vector similarity search with full-text search

• Claude Desktop
• Claude Code
• Goose
• OpenAI ChatGPT (chat.openai.com)
• OpenAI Playground

{
  "mcpServers": {
    "build-vault": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.buildaipod.com/mcp"]
    }
  }
}

claude mcp add build-vault -s user --transport http https://mcp.buildaipod.com/mcp

• Protocol Version: 2025-06-18 with full specification compliance
• Structured Output: Enhanced tools with outputSchema and structuredContent
• Elicitation Support: Declared capability with intelligent follow-up suggestions
• Title Fields: All tools, resources, and prompts include descriptive titles
• Resource Links: Cross-referencing between related content
• Transports: stdio + Streamable HTTP

• OpenAI Deep Research: Compatible with OpenAI's Deep Research Custom Connectors

• List Products: Browse AI products with filtering and pagination
• Search Products: Semantic search using embeddings (3072 dimensions)
• Product Details: Comprehensive product information with resource links
• Find Similar: Vector similarity search for related products
• Search by Speaker: Filter insights by podcast speakers (Tom Spencer, Cameron Rohn)
• Search by Date Range: Find products within specific time periods
• Search by Category: Filter by 6 content categories (business_ideas, frameworks_and_exercises, products, points_of_view, stories_and_anecdotes, quotes)
• Search by Timeframe: Find insights within episode timestamps
• Speaker Summary: Comprehensive speaker statistics and insights
• Timeline Insights: Chronologically ordered insights with metadata
• Search (Deep Research): Natural language search for AI insights and episodes
• Fetch (Deep Research): Get complete content with full context and metadata

• Trending Insights: High-confidence insights with smart follow-up suggestions
• Category Distribution: Live analytics on content breakdown by category
• Episode Timeline: Chronological episode data with insight counts
• Speaker Analytics: Real-time speaker statistics and content analysis

• Find Business Ideas: Discover business insights and opportunities
• Explore Frameworks: Structured exploration of frameworks and exercises
• Timeline Analysis: Chronological exploration of topics and themes
• Compare Content Types: Compare different categories of insights

When accessing resources, the server provides intelligent follow-up suggestions:

• Category Analysis: "Found 9 product insights, 6 points_of_view insights"
• Speaker Breakdown: "Cameron Rohn (11 insights), Tom Spencer (8 insights)"
• Tool Recommendations: Specific next-step suggestions with usage examples
• Semantic Search Guidance: Query suggestions based on actual content

This server is compatible with OpenAI's Deep Research Custom Connectors. The search and fetch tools are specifically designed to work with Deep Research models:

• Search Tool: Accepts natural language queries (e.g., "insights about AI agents") and returns results in the format {id, title, text, url}
• Fetch Tool: Retrieves complete content with metadata for deep analysis and citation

1. Browse Categories: Use search_by_category with "products" to see 334 product insights
2. Semantic Search: Try search_products with "AI agents" or "LangChain"
3. Trending Content: Access vault://trending_insights resource for top 20 high-confidence insights
4. Follow Suggestions: Look for "What's Next?" sections with intelligent recommendations

Try these searches to get started:

• "What frameworks exist for prompt engineering?"
• "Business ideas in the healthcare AI space"
• "What did Tom Spencer say about LangChain?"
• "Insights about AI safety and alignment"
• "Products for building chatbots"

• Triple Transport Design: stdio, HTTP, and Cloudflare Workers
• Type Safety: TypeScript with Zod runtime validation
• Vector Search: Real-time semantic similarity
• Elicitation: Intelligent follow-up suggestions based on content analysis
• Health Monitoring: Built-in health check endpoints
• Deep Research Compatible: Implements search/fetch tools for OpenAI integration

• DB Tables: Index with vector embeddings
• Content: Thousands of AI insights from vault.buildaipod.com
• Categories: 6 types (business_ideas, frameworks_and_exercises, products, points_of_view)
• Embeddings: 3072-dimensional vectors from text-embedding-3-large

• Version: 0.2.0
• Protocol: MCP 2025-06-18
• SDK: @modelcontextprotocol/sdk 1.16.0
• Features: Full specification compliance with elicitation, structured output, resource links, and Deep Research compatibility
• stdio: Default MCP transport for direct client integration
• http: MCP 2025-06-18 Streamable HTTP with header validation

• MCP Central Lab: Test the server interactively at https://lab.mcpcentral.io/

This server is published in the official Model Context Protocol Registry. The registry configuration is defined in server.json, which specifies:

• Server Metadata: Name, description, and repository information
• Remote Endpoints: HTTP transport endpoints at https://mcp.buildaipod.com/mcp and https://mcp.demos.build/mcp
• Package Distribution: Available on npm as build-vault-mcp-server
• Client Compatibility: Supports Claude Desktop, Claude Code, Goose, and OpenAI ChatGPT
• Feature Declaration: 12 tools, 8 resources, 4 prompts with semantic search and deep research capabilities

The registry enables automatic discovery and installation of this MCP server across compatible clients.

• GitHub Issues: For bug reports and feature requests
• Health Check: GET /health endpoint for status monitoring

Scenario: A developer wants to research AI agents and autonomous systems to build their own agent framework.

Tools Used: search, fetch, search_by_speaker

1. Initial Search: Search for "AI agents and autonomous systems"
2. Get Detailed Content: Fetch the full content for a specific insight ID
3. Find Expert Perspectives: Search for insights by speaker "Tom Spencer"

Expected Results: Framework discussions, real-world implementations, and expert opinions on agent architecture.

Scenario: An entrepreneur wants to find validated business ideas in the AI space discussed by industry experts.

Tools Used: search_by_category, find_similar_products, get_speaker_summary

1. Browse Business Ideas: Search by category "business_ideas"
2. Find Similar Concepts: Find products similar to interesting results
3. Expert Analysis: Get comprehensive summary for speaker "Cameron Rohn"

Expected Results: SaaS opportunities, AI product concepts, and market validation insights.

Scenario: A product manager needs proven frameworks for building AI products and managing development processes.

Tools Used: search_by_category, get_timeline_insights, search_by_date_range

1. Find Frameworks: Search by category "frameworks_and_exercises"
2. See Evolution Over Time: Get timeline insights for 2024
3. Recent Best Practices: Search by recent date range

Expected Results: Product development methodologies, AI implementation strategies, and team management approaches.