Pinecone Vector DB▌
by sirmews
Leverage Pinecone vector database for fast semantic search and retrieval augmented generation (RAG) with scalable vector
Leverage Pinecone vector databases for semantic search and RAG.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / AI developers building RAG applications
- / Teams implementing semantic search features
- / Researchers working with document similarity
- / Chatbot developers needing knowledge retrieval
capabilities
- / Search documents by semantic similarity
- / Store and index documents as vectors
- / Read specific documents from the database
- / List all available documents
- / Generate embeddings for text content
- / View database statistics and metadata
what it does
Connects to Pinecone vector databases to store, search, and retrieve documents using semantic similarity. Enables building RAG (Retrieval Augmented Generation) applications with vector embeddings.
about
Pinecone Vector DB is a community-built MCP server published by sirmews that provides AI assistants with tools and capabilities via the Model Context Protocol. Leverage Pinecone vector database for fast semantic search and retrieval augmented generation (RAG) with scalable vector It is categorized under databases, ai ml.
how to install
You can install Pinecone Vector DB in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Pinecone Vector DB is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Pinecone Model Context Protocol Server for Claude Desktop.
Read and write to a Pinecone index.
Components
flowchart TB
subgraph Client["MCP Client (e.g., Claude Desktop)"]
UI[User Interface]
end
subgraph MCPServer["MCP Server (pinecone-mcp)"]
Server[Server Class]
subgraph Handlers["Request Handlers"]
ListRes[list_resources]
ReadRes[read_resource]
ListTools[list_tools]
CallTool[call_tool]
GetPrompt[get_prompt]
ListPrompts[list_prompts]
end
subgraph Tools["Implemented Tools"]
SemSearch[semantic-search]
ReadDoc[read-document]
ListDocs[list-documents]
PineconeStats[pinecone-stats]
ProcessDoc[process-document]
end
end
subgraph PineconeService["Pinecone Service"]
PC[Pinecone Client]
subgraph PineconeFunctions["Pinecone Operations"]
Search[search_records]
Upsert[upsert_records]
Fetch[fetch_records]
List[list_records]
Embed[generate_embeddings]
end
Index[(Pinecone Index)]
end
%% Connections
UI --> Server
Server --> Handlers
ListTools --> Tools
CallTool --> Tools
Tools --> PC
PC --> PineconeFunctions
PineconeFunctions --> Index
%% Data flow for semantic search
SemSearch --> Search
Search --> Embed
Embed --> Index
%% Data flow for document operations
UpsertDoc --> Upsert
ReadDoc --> Fetch
ListRes --> List
classDef primary fill:#2563eb,stroke:#1d4ed8,color:white
classDef secondary fill:#4b5563,stroke:#374151,color:white
classDef storage fill:#059669,stroke:#047857,color:white
class Server,PC primary
class Tools,Handlers secondary
class Index storage
Resources
The server implements the ability to read and write to a Pinecone index.
Tools
semantic-search: Search for records in the Pinecone index.read-document: Read a document from the Pinecone index.list-documents: List all documents in the Pinecone index.pinecone-stats: Get stats about the Pinecone index, including the number of records, dimensions, and namespaces.process-document: Process a document into chunks and upsert them into the Pinecone index. This performs the overall steps of chunking, embedding, and upserting.
Note: embeddings are generated via Pinecone's inference API and chunking is done with a token-based chunker. Written by copying a lot from langchain and debugging with Claude.
Quickstart
Installing via Smithery
To install Pinecone MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install mcp-pinecone --client claude
Install the server
Recommend using uv to install the server locally for Claude.
uvx install mcp-pinecone
OR
uv pip install mcp-pinecone
Add your config as described below.
Claude Desktop
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Note: You might need to use the direct path to uv. Use which uv to find the path.
Development/Unpublished Servers Configuration
"mcpServers": {
"mcp-pinecone": {
"command": "uv",
"args": [
"--directory",
"{project_dir}",
"run",
"mcp-pinecone"
]
}
}
Published Servers Configuration
"mcpServers": {
"mcp-pinecone": {
"command": "uvx",
"args": [
"--index-name",
"{your-index-name}",
"--api-key",
"{your-secret-api-key}",
"mcp-pinecone"
]
}
}
Sign up to Pinecone
You can sign up for a Pinecone account here.
Get an API key
Create a new index in Pinecone, replacing {your-index-name} and get an API key from the Pinecone dashboard, replacing {your-secret-api-key} in the config.
Development
Building and Publishing
To prepare the package for distribution:
- Sync dependencies and update lockfile:
uv sync
- Build package distributions:
uv build
This will create source and wheel distributions in the dist/ directory.
- Publish to PyPI:
uv publish
Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token:
--tokenorUV_PUBLISH_TOKEN - Or username/password:
--username/UV_PUBLISH_USERNAMEand--password/UV_PUBLISH_PASSWORD
Debugging
Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.
You can launch the MCP Inspector via npm with this command:
npx @modelcontextprotocol/inspector uv --directory {project_dir} run mcp-pinecone
Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Source Code
The source code is available on GitHub.
Contributing
Send your ideas and feedback to me on Bluesky or by opening an issue.
FAQ
- What is the Pinecone Vector DB MCP server?
- Pinecone Vector DB is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Pinecone Vector DB?
- This profile displays 45 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Direct Database Queries from AI
Enable Claude to query your database directly using natural language
Example
Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly
Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster
Data Analysis & Reporting
Generate complex reports and analytics without leaving conversation
Example
Analyze sales trends, cohort retention, user behavior patterns conversationally
Democratize data access—non-technical team members can query databases
Schema Exploration
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Data Validation & Quality Checks
Run data quality queries to catch anomalies and inconsistencies
Example
Find duplicate records, missing values, orphaned foreign keys automatically
Maintain data integrity with less manual SQL work
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor with MCP support
- ›Database credentials (read-only recommended for safety)
- ›Network access from Claude client to database
- ›Understanding of database security and access control
Time Estimate
15-30 minutes including configuration and testing
Installation Steps
- 1.Install MCP server: npm install -g @modelcontextprotocol/server-[name]
- 2.Configure database connection in Claude Desktop config (~/.claude/mcp.json)
- 3.Provide connection string: host, port, database, username, password
- 4.Restart Claude Desktop to load MCP server
- 5.Test connection: 'List all tables in database'
- 6.Run simple query: 'Show me 5 rows from users table'
- 7.Verify results and permissions are correct
- 8.Document query patterns for team use
Troubleshooting
- ⚠Connection refused: Check database is running and network accessible
- ⚠Authentication failed: Verify credentials, check user permissions
- ⚠Claude can't see tables: Grant appropriate read permissions to database user
- ⚠Slow queries: Add indexes, limit result set size, use read replicas
- ⚠MCP server not loading: Check config syntax, restart Claude Desktop
Best Practices▌
✓ Do
- +Use read-only database credentials to prevent accidental writes
- +Connect to read replica, not production primary database
- +Set query timeout limits to prevent long-running queries
- +Document database schema and common queries for AI context
- +Monitor query performance and optimize slow queries
- +Use connection pooling for better performance
- +Test with non-production data first
✗ Don't
- −Don't use production write credentials—risk of data corruption
- −Don't query production database during peak traffic hours
- −Don't expose sensitive PII without proper access controls
- −Don't skip query result validation—AI can misinterpret schema
- −Don't allow unlimited result set sizes—set LIMIT clauses
- −Don't share database credentials in plain text config files
💡 Pro Tips
- ★Create database views for common queries to simplify AI access
- ★Add schema comments/descriptions so AI understands column meanings
- ★Use semantic table/column names ('customer_lifetime_value' not 'clv')
- ★Set up query logging to audit what Claude is querying
- ★Create saved query templates for recurring analysis
- ★Combine with data visualization tools for better insights
Technical Details▌
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
- Model Context Protocol (MCP)
- Database-specific protocols (PostgreSQL, MySQL, MongoDB)
Compatibility
- PostgreSQL
- MySQL
- SQLite
- MongoDB
- Redis
When to Use This▌
✓ Use When
Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.
✗ Avoid When
Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.
Integration▌
- →Read replica connection for analytics queries
- →Database view layer to abstract complex joins
- →Query result caching for repeated questions
- →Audit logging of all AI-generated queries
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.7★★★★★45 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
Pinecone Vector DB has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Omar Sanchez· Dec 28, 2024
We wired Pinecone Vector DB into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Zara Kapoor· Dec 24, 2024
According to our notes, Pinecone Vector DB benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Lucas Kapoor· Nov 19, 2024
Pinecone Vector DB is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Arjun Farah· Nov 15, 2024
Useful MCP listing: Pinecone Vector DB is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Aarav Srinivasan· Oct 10, 2024
Useful MCP listing: Pinecone Vector DB is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Mia Martinez· Oct 6, 2024
Pinecone Vector DB is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Nia Verma· Sep 25, 2024
Pinecone Vector DB is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Nia Tandon· Sep 21, 2024
Pinecone Vector DB has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Oshnikdeep· Sep 17, 2024
Pinecone Vector DB reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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