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Pinecone
Vector Search Database & StorageInstall Command
npx clawhub@latest install pinecone
Installation Guide
1
Check Environment
Make sure Node.js 22+ and OpenClaw are installed. Run openclaw --version in your terminal to verify.
2
Run Installation
Run the install command above in your terminal. ClawHub will automatically download and install Pinecone to the ~/.openclaw/skills/ directory.
3
Verify Installation
Run openclaw skills list to check your installed skills and confirm Pinecone appears in the list.
4
Configure (Optional)
Follow the configuration instructions in the description below to add skill settings to ~/.config/openclaw/openclaw.json5.
Manual Installation: Copy the Skill folder to
~/.openclaw/skills/ or the skills/ directory in your project root. Make sure the folder contains a SKILL.md file.
Managed vector index management
High-performance semantic search
Namespace isolation and metadata filtering
Detailed Description
Pinecone MCP server integrates the Pinecone managed vector database, providing high-performance vector storage and search capabilities to build production-grade semantic search and RAG applications without managing infrastructure.
Core Features
- Index Management: Create and manage Pinecone indexes, configure vector dimensions, metric types, and Pod/Serverless types
- Vector Operations (upsert): Batch insert or update vector data with metadata and namespace isolation support
- Semantic Query (query): Top-K search based on vector similarity with metadata filtering and namespace scoping
- Data Management: Get, update, and delete vectors by ID, with support for batch deletion by metadata filter
- Statistics (describe_index_stats): View vector count, dimensions, and namespace distribution for an index
Configuration
{
"mcpServers": {
"pinecone": {
"command": "npx",
"args": ["-y", "@pinecone-database/mcp-server"],
"env": {
"PINECONE_API_KEY": "your-api-key" // Get from Pinecone Console
}
}
}
}
Use Cases
- Enterprise knowledge base: Build semantic search systems based on internal documents
- Customer support Q&A: Match user questions to the most relevant knowledge base answers
- Recommendation system: Personalized recommendations based on user profile vectors
- Code search: Store code snippet vectors for semantic-level code search