Query, upsert, and manage vectors in your Pinecone indexes — AI-native vector database access for semantic search and RAG.
Use with AI AssistantsMCP
Connect Claude, Cursor, or any MCP-compatible client — then call tools directly
① Add This MCP Server
Paste into your AI client config — then all its tools are available instantly.
{
"mcpServers": {
"pinecone": {
"url": "https://mcp.aerostack.dev/s/aerostack/mcp-pinecone",
"headers": {
"Authorization": "Bearer YOUR_AEROSTACK_TOKEN"
}
}
}
}Replace YOUR_AEROSTACK_TOKEN with your API token from the dashboard.
② Call a Tool
Ask your AI assistant to call a specific tool, or send raw JSON-RPC:
Natural Language Prompt
“Use the _ping tool to verify pinecone connectivity by listing indexes. used internally by aerostack to validate credentials”
Using a Workspace?
Add this MCP to your Workspace — your team shares one token, secrets are stored securely, and every AI agent in the workspace can call it without per-user setup.
Query, upsert, and manage vectors in your Pinecone indexes from your AI agents.
Pinecone is the leading vector database for AI/ML applications — purpose-built for high-performance similarity search at scale. This MCP server gives your AI agents full access to your Pinecone indexes: querying vectors for semantic search and RAG, upserting embeddings, managing namespaces, and inspecting index statistics — all through natural language.
Live endpoint: https://mcp.aerostack.dev/s/aerostack/mcp-pinecone
| Tool | Description |
|---|---|
list_indexes | List all indexes with status, dimension, metric, and host |
describe_index | Get detailed info for a specific index (host, dimension, status) |
query | Query vectors by embedding or ID with filters, topK, and metadata |
upsert | Upsert vectors with IDs, values, and optional metadata |
fetch | Fetch full vectors by their IDs |
delete_vectors | Delete vectors by IDs, metadata filter, or delete all |
describe_stats | Get index statistics — total vectors, per-namespace counts |
list_vectors | List vector IDs in a namespace with pagination |
update_vector | Update a vector's values or metadata by ID |
| Variable | Required | Description | How to Get |
|---|---|---|---|
PINECONE_API_KEY | Yes | Your Pinecone API key | app.pinecone.io → API Keys → copy your key |
PINECONE_API_KEY under Project → SecretsOnce added, every AI agent in your workspace can call Pinecone tools automatically — no per-user setup needed.
"List all my Pinecone indexes and show their dimensions and status"
"Query my product-embeddings index for the top 5 vectors similar to this text embedding"
"Upsert these 3 document embeddings into the knowledge-base index with their source metadata"
"Show me the stats for my main index — how many vectors per namespace?"
curl -X POST https://mcp.aerostack.dev/s/aerostack/mcp-pinecone \
-H 'Content-Type: application/json' \
-H 'X-Mcp-Secret-PINECONE-API-KEY: your-pinecone-api-key' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"list_indexes","arguments":{}}}'
MIT
Live Endpoint
https://mcp.aerostack.dev/s/aerostack/mcp-pinecone
Sub-50ms globally · Zero cold start
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Yes. describe_index returns the index dimension, metric, and pod configuration. Claude can verify compatibility before upserting vectors to avoid silent dimension-mismatch errors in Pinecone.
The query and upsert tools both accept an optional namespace parameter. Claude can isolate tenant data or document sets within one Pinecone index by routing reads and writes to separate namespaces.
Yes. update_vector lets Claude patch the metadata or sparse values of a stored vector by ID. The original embedding is preserved, saving re-embedding costs for metadata-only changes.
describe_stats returns total vector count and per-namespace breakdowns. Claude can compare this against the index's configured pod capacity to warn before Pinecone rejects new upserts.