How to Use the Pinecone MCP in LangChain

Q: How do I add the Pinecone MCP Server to my LangChain setup?

Install langchain-mcp-adapters and langgraph via pip. Initialize the client using MultiServerMCPClient with the Vinkius URL, then pass the tools from client.gettools() straight into your agent constructor.

Q: Can I monitor Pinecone tool performance inside LangSmith?

Yes, easily. Every single MCP tool call, whether it is getindexstats or queryvectors, is recorded as a distinct step in your LangSmith trace, showing you execution times and payload sizes.

Run multi-step vector workflows natively in LangChain by hooking your chains directly to Pinecone indexes.

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Connect Pinecone MCP to LangChain

Create your Vinkius account to connect Pinecone to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Pinecone with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Trace Pinecone operations in LangChain

When your agent executes vector operations, you need to see exactly what went down. By combining this MCP Server with LangSmith, you trace every call to `query_vectors` or `fetch_vectors` in real time, exposing latencies and token costs without guessing. You construct chains where the output of a `get_index_stats` call feeds directly into your next retrieval step. This keeps your LangChain pipelines grounded in actual database metrics instead of blind execution.

Dynamic index switching in ReAct loops

Stop hardcoding index targets in your Python files. This MCP integration lets your LangChain agent run `list_indexes` and `describe_index` to inspect available environments and select the correct target on the fly. If a target index is running hot or empty, the agent detects it through `get_index_stats` and adjusts its routing logic. You write the high-level chain, and the database state dictates the execution path.

Automated index housecleaning

Keeping vector stores clean usually requires writing bespoke cron jobs. With this setup, your LangChain agent evaluates vector age or relevance and invokes `delete_vectors` to purge stale embeddings automatically. The agent coordinates these deletions by first checking `list_collections` to verify it isn't touching critical archived data. You get a self-maintaining vector pipeline managed entirely through natural language chains.

Setup guide

Set up Pinecone MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Pinecone tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "pinecone-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Pinecone transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Pinecone. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Pinecone MCP in LangChain

Install `langchain-mcp-adapters` and `langgraph` via pip. Initialize the client using `MultiServerMCPClient` with the Vinkius URL, then pass the tools from `client.get_tools()` straight into your agent constructor.

Yes, that is the core strength of this setup. Your agent can run `query_vectors` to find similar records, evaluate the results, and then call `delete_vectors` if those records are flagged for removal.

Your LangChain agent handles this through standard runnable configs or custom error-handling chains. If `fetch_vectors` hits an API limit, the agent catches the exception and retries the operation based on your chain policy.

Yes, easily. Every single MCP tool call, whether it is `get_index_stats` or `query_vectors`, is recorded as a distinct step in your LangSmith trace, showing you execution times and payload sizes.

The server runs in a zero-trust V8 sandbox on Vinkius, meaning your raw embeddings and metadata are never stored or exposed. Your API keys are injected securely at runtime, keeping your vector payloads isolated from external eyes.

Use it with your favorite AI tools

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