Build Vault MCP Server

🚀 The Build Vault MCP Server

Transform The Build Podcast into a searchable knowledge base with thousands of AI insights using advanced hybrid search.

🚀 Quick Start

The Model Context Protocol (MCP) server combines vector semantic similarity with full - text search. You can access the collective wisdom of builders and entrepreneurs through natural language queries, making podcast knowledge instantly actionable.

✨ Features

• Transforms The Build Podcast into a searchable knowledge base.
• Combines vector semantic similarity with full - text search.
• Helps discover business ideas, frameworks, and product strategies.
• Allows access to podcast knowledge through natural language queries.

📦 Installation

There is no specific installation information provided in the original README. However, for client configuration, you can refer to the "MCP Client Configuration" section.

💻 Usage Examples

Basic Usage

Discovering AI Products

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.

Advanced Usage

Example 1: AI Agent Research for Developers

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.

Example 2: Business Idea Discovery for Entrepreneurs

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.

Example 3: Framework Research for Product Managers

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.

📚 Documentation

Background

Our MCP Server sources its 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

MCP Client Configuration

Known Client Compatibility:

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

Claude Desktop

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

Claude Code

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

MCP Version Compatability

MCP 2025 - 06 - 18 Compliance

• 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

Enhanced Compatability

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

Vault Discovery Tools (12 Total)

• 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

Analytics Resources with Elicitation (4 Total)

• 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

Guided Prompts (4 Total)

• 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

Enhanced Elicitation Features

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

OpenAI Deep Research Integration

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

Available Tools

Available Resources

Available Prompts

🔧 Technical Details

Architecture

Key Technical Features

• 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

Data Overview

Primary Data Source: The Build Vault Database

• 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 Information

• 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

📄 License

There is no license information provided in the original README, so this section is skipped.

Testing

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

MCP Registry

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.

Support

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