mcp-registry/mcp-pinecone

    ==================
      
       /// MCP ///
      /// MCP ///
        
    ==================
        
    [server:online]
    [protocol:ready]

mcp-pinecone

by sirmews

Model Context Protocol (MCP) server that lets Claude Desktop (or any MCP client) read from and write to a Pinecone vector index. Provides basic RAG-style tools such as semantic-search, read-document, list-documents, pinecone-stats and process-document.

135
28
Open Source
GitHub

01

semantic-search

Search for records in the Pinecone index.

02

read-document

Read a document from the Pinecone index.

03

list-documents

List all documents in the Pinecone index.

04

pinecone-stats

Get stats about the Pinecone index, including the number of records, dimensions, and namespaces.

05

process-document

Process a document into chunks and upsert them into the Pinecone index, performing chunking, embedding, and upserting.

Installation

1. Clone the repository
git clone https://github.com/sirmews/mcp-pinecone.git && cd mcp-pinecone
2. Create an isolated Python environment (recommended)
python -m venv .venv && source .venv/bin/activate
3. Install Python dependencies
pip install -r requirements.txt
4. Export the required environment variables so the server can reach Pinecone
export PINECONE_API_KEY=<your-pinecone-key>
export PINECONE_ENVIRONMENT=<us-east1-gcp / eu-west1-gcp / etc>
# Optional – change default server port
export MCP_PORT=8000
5. Initialise the Pinecone index (first-time only)
python scripts/init_index.py # <— if such helper script exists; otherwise create index in dashboard>
6. Start the MCP server
python -m mcp_pinecone # or
uvicorn mcp_pinecone.server:app --host 0.0.0.0 --port ${MCP_PORT:-8000}
7. Verify it is running
curl http://localhost:8000/health

Documentation

# Transform Augment Code into a Knowledge-Aware Coding Assistant

The mcp-pinecone server turns Augment Code into a semantic search powerhouse, giving your AI assistant instant access to your entire codebase, documentation, and project knowledge through vector-based retrieval. Instead of just generating code from scratch, Augment can now find relevant examples, documentation, and patterns from your existing projects to provide contextually-aware suggestions and solutions.

## Supercharge Your Coding Workflow with Semantic Context

With this MCP server configured in Augment Code, you can ask questions like "Show me how we handled authentication in our previous React projects" or "Find examples of error handling patterns in our API layer" - and Augment will semantic-search through your indexed codebase to find the most relevant examples. The `semantic-search` tool lets Augment query your Pinecone index with natural language, while `read-document` retrieves specific code snippets or documentation. When you're working on a new feature, Augment can use `list-documents` to understand what knowledge is available and `process-document` to add new code patterns to your searchable knowledge base as you build them.

## Real-World Developer Productivity Gains

Picture this: you're building a new microservice and need to remember how your team handles database connections. Instead of manually digging through repos, you ask Augment "How do we typically set up database connections in our Node.js services?" Augment searches your indexed codebase, finds the relevant patterns, and not only shows you the code but explains the reasoning behind your team's approach. The `pinecone-stats` tool helps Augment understand the scope of your knowledge base, while the RAG capabilities mean every code suggestion is grounded in your actual project patterns rather than generic examples. You're not just getting an AI coding assistant - you're getting an AI that understands your codebase's DNA and can apply that knowledge to accelerate your development workflow.
License: MIT License
Updated 7/15/2025

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