How to Use the Pinecone MCP in LlamaIndex

Q: How do I configure the Pinecone MCP Server within LlamaIndex?

Install the LlamaIndex MCP adapter and instantiate BasicMCPClient with your Vinkius endpoint. Convert those to LlamaIndex tools using McpToolSpec and pass them directly to your FunctionAgent.

Q: Can LlamaIndex query Pinecone namespaces dynamically?

Yes. Your agent can run getindexstats to see which namespaces exist, then direct its queryvectors calls to the specific namespace containing the relevant embeddings.

Q: How does LlamaIndex handle empty search results from Pinecone?

When queryvectors returns an empty list, the LlamaIndex agent recognizes the empty payload and can fallback to alternative indexes or report the missing context to your pipeline.

Q: Is it possible to delete vectors using LlamaIndex agents?

Yes, you can expose deletevectors to your agent. This allows the LlamaIndex agent to prune old or low-similarity embeddings directly during its indexing cycles.

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Setup Pinecone with LlamaIndex

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Semantic memory indexing via LlamaIndex

LlamaIndex excels at turning raw API responses into queryable knowledge. This MCP Server lets your agent run `query_vectors` and immediately index those retrieved coordinates and metadata back into your local document store. Instead of treating database responses as transient chat history, the agent structures them. It uses `fetch_vectors` to pull specific records, transforming raw database floats into searchable, persistent semantic indexes.

Context-aware index monitoring

Before executing complex semantic queries, your LlamaIndex agent needs to know what it is working with. Running `get_index_stats` allows the agent to check vector dimensions and namespace density before choosing a retrieval strategy. The agent can inspect available collections with `list_collections` to find archived data. This ensures your RAG pipelines never query mismatched vector dimensions or dead namespaces.

Automated vector index discovery

Hardcoded index names break when you scale your database environments. Your agent uses `list_indexes` and `describe_index` to discover active environments and dynamically configure its own query engines. This self-configuring behavior means your LlamaIndex pipelines adapt automatically when you deploy new indexes. The agent reads the index parameters and instantiates the correct MCP tool configuration without manual code deployments.

Setup guide

Set up Pinecone MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Pinecone MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Pinecone tools.",
)
response = await agent.run("List recent Pinecone data")

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Common questions about Pinecone MCP in LlamaIndex

Install the LlamaIndex MCP adapter and instantiate `BasicMCPClient` with your Vinkius endpoint. Convert those to LlamaIndex tools using `McpToolSpec` and pass them directly to your `FunctionAgent`.

Yes. Your agent can run `get_index_stats` to see which namespaces exist, then direct its `query_vectors` calls to the specific namespace containing the relevant embeddings.

When `query_vectors` returns an empty list, the LlamaIndex agent recognizes the empty payload and can fallback to alternative indexes or report the missing context to your pipeline.

Yes, you can expose `delete_vectors` to your agent. This allows the LlamaIndex agent to prune old or low-similarity embeddings directly during its indexing cycles.

All communications pass through Vinkius's secure, ephemeral V8 sandboxes. Your raw vector coordinates and metadata payloads are never cached, and your API keys are encrypted at rest and only used to authenticate direct requests.

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