Pinecone Assistant MCP Server
Integrate Pinecone Assistant’s semantic search, multi-result retrieval, and knowledge base access into your AI agents with this secure MCP server.
What does “Pinecone Assistant” MCP Server do?
The Pinecone Assistant MCP Server is a Model Context Protocol (MCP) server implementation designed to retrieve information from Pinecone Assistant. It enables AI assistants to connect with the Pinecone vector database and its assistant features, allowing for enhanced development workflows such as semantic search, information retrieval, and multi-result queries. By acting as a bridge between AI clients and the Pinecone Assistant API, it empowers tasks like searching knowledge bases, responding to queries, and integrating vector database capabilities into broader AI workflows. The server is configurable and can be deployed via Docker or built from source, making it suitable for integration into various AI development environments.
List of Prompts
No prompt templates are mentioned in the available documentation or repository files.
List of Resources
No explicit resources are described in the available documentation or repository files.
List of Tools
No explicit tools or tool names are described in the available documentation or repository files.
Use Cases of this MCP Server
- Semantic Search Integration: Developers can enhance AI agents with the ability to perform semantic searches over large datasets using Pinecone’s vector search capabilities.
- Knowledge Base Querying: Build assistants that retrieve contextually relevant information from organizational knowledge bases stored in Pinecone.
- Multi-result Retrieval: Configure and retrieve multiple relevant results for user queries, improving AI assistant response quality.
- AI Workflow Enhancement: Integrate the MCP server into existing development tools (such as Claude or Cursor) to provide AI agents with real-time access to external knowledge and vector search.
- Secure API Access: Manage API keys and endpoints securely while leveraging Pinecone Assistant for various development and research tasks.
How to set it up
Windsurf
No Windsurf-specific installation instructions are provided in the available documentation.
Claude
- Ensure you have Docker installed.
- Obtain your Pinecone API key from the Pinecone Console.
- Find your Pinecone Assistant API host (from the Assistant details page in the console).
- Add the following to your
claude_desktop_config.json:
{
"mcpServers": {
"pinecone-assistant": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"PINECONE_API_KEY",
"-e",
"PINECONE_ASSISTANT_HOST",
"pinecone/assistant-mcp"
],
"env": {
"PINECONE_API_KEY": "<YOUR_PINECONE_API_KEY_HERE>",
"PINECONE_ASSISTANT_HOST": "<YOUR_PINECONE_ASSISTANT_HOST_HERE>"
}
}
}
}
- Save the configuration and restart Claude Desktop.
Securing API keys
API keys and sensitive environment variables are set in the env block as shown above, keeping them out of the command line and configuration files.
Cursor
No Cursor-specific installation instructions are provided in the available documentation.
Cline
No Cline-specific installation instructions are provided in the available documentation.
How to use this MCP inside flows
Using MCP in FlowHunt
To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:
Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:
{
"pinecone-assistant": {
"transport": "streamable_http",
"url": "https://yourmcpserver.example/pathtothemcp/url"
}
}
Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “pinecone-assistant” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.
Overview
| Section | Availability | Details/Notes |
|---|---|---|
| Overview | ✅ | Overview and features available in README.md |
| List of Prompts | ⛔ | No prompt templates found in documentation or repo |
| List of Resources | ⛔ | No explicit resources described |
| List of Tools | ⛔ | No explicit tool definitions found |
| Securing API Keys | ✅ | Usage of env block in Claude config example |
| Sampling Support (less important in evaluation) | ⛔ | No mention of sampling capability |
Our opinion
Based on the available documentation, the Pinecone Assistant MCP server is well-documented for setup and basic usage, but lacks detail on prompt templates, resources, and tools specific to the MCP protocol. It is easy to integrate with Claude Desktop and provides guidance on securing API keys, but may require more MCP-specific features and documentation for comprehensive use.
Score: 5/10
The MCP server is solid for Pinecone integration and security, but documentation gaps in MCP-specific primitives and features limit its broader utility.
MCP Score
| Has a LICENSE | ✅ |
|---|---|
| Has at least one tool | ⛔ |
| Number of Forks | 4 |
| Number of Stars | 20 |
Frequently asked questions
- What does the Pinecone Assistant MCP Server do?
It connects AI assistants to Pinecone's vector database, enabling semantic search, knowledge retrieval, and multi-result responses for enhanced AI workflows.
- How do I configure the Pinecone Assistant MCP Server?
For Claude Desktop, use Docker and provide your Pinecone API key and Assistant host in the configuration file. See the configuration section for a sample JSON setup.
- Does the MCP server support secure API key handling?
Yes. API keys and sensitive values are set via environment variables in the configuration file, keeping them secure and separated from code.
- What are typical use cases?
Semantic search over large datasets, querying organizational knowledge bases, retrieving multiple relevant results, and integrating vector search into AI workflows.
- Is there support for other clients like Windsurf or Cursor?
No specific setup instructions are provided for Windsurf or Cursor, but you can adapt the general MCP configuration for your environment.
Integrate Pinecone Assistant MCP with FlowHunt
Boost your AI agent's capabilities by connecting to Pinecone's vector database using the Pinecone Assistant MCP Server. Try it with FlowHunt or your favorite development tool for advanced search and knowledge retrieval.
Learn more
