by sirmews
Leverage Pinecone vector database for fast semantic search and retrieval augmented generation (RAG) with scalable vector
Connects to Pinecone vector databases to store, search, and retrieve documents using semantic similarity. Enables building RAG (Retrieval Augmented Generation) applications with vector embeddings.
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.
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.
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.
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
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
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Share your MCP server with the developer community
Pinecone Vector DB has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We wired Pinecone Vector DB into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, Pinecone Vector DB benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Pinecone Vector DB is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: Pinecone Vector DB is the kind of server we cite when onboarding engineers to host + tool permissions.
Useful MCP listing: Pinecone Vector DB is the kind of server we cite when onboarding engineers to host + tool permissions.
Pinecone Vector DB is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Pinecone Vector DB is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Pinecone Vector DB has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Pinecone Vector DB reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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Read and write to a Pinecone index.
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
The server implements the ability to read and write to a Pinecone index.
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.
To install Pinecone MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install mcp-pinecone --client claude
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.
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"
]
}
}
You can sign up for a Pinecone account here.
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.
To prepare the package for distribution:
uv sync
uv build
This will create source and wheel distributions in the dist/ directory.
uv publish
Note: You'll need to set PyPI credentials via environment variables or command flags:
--token or UV_PUBLISH_TOKEN--username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORDSince 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.
This project is licensed under the MIT License. See the LICENSE file for details.
The source code is available on GitHub.
Send your ideas and feedback to me on Bluesky or by opening an issue.
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
Prerequisites
Time Estimate
15-30 minutes including configuration and testing
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
Compatibility
✓ 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.