Pinecone vector database integration for Codex. Streamline your Pinecone development with reusable skills for managing vector indexes, querying data, building RAG applications, and rapid prototyping. Includes the Pinecone MCP server for creating, describing, upserting, and searching indexes directly from Codex. Perfect for developers building semantic search, RAG applications, recommendation systems, and other vector-based applications with Pinecone.
This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.
What it installs
Skills (9)
assistant
Create, manage, and chat with Pinecone Assistants for document Q&A with citations. Handles all assistant operations - create, upload, sync, chat, context retrieval, and list. Recognizes natural language like "create an assistant from my docs", "ask my assistant about X", or "upload my docs to Pineco
cli
Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, C
docs
Curated documentation reference for developers building with Pinecone. Contains links to official docs organized by topic and data format references. Use when writing Pinecone code, looking up API parameters, or needing the correct format for vectors or records.
full-text-search
Create, ingest into, and query a Pinecone full-text-search (FTS) document index using the graduated document-schema API (Python SDK 10.1.0+, API version 2026-07). Use when the user or agent asks to build a text search index on Pinecone, have Pinecone embed a text field (integrated embedding), add de
help
Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill.
mcp
Reference for the Pinecone MCP server tools. Documents all available tools - list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. Use when an agent needs to understand what Pinecone MCP tools are available
n8n
Build n8n workflows using the Pinecone Assistant node or Pinecone Vector Store node. Use when building RAG pipelines, chat-with-docs workflows, configuring Pinecone nodes in n8n, troubleshooting Pinecone n8n nodes, or asking about best practices for Pinecone in n8n.
query
Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the pinecone:cli skill instead. Requires PINECONE_API
quickstart
Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create a document index with integrated embedding, upsert sample documents, and search them by meaning and by exact terms, using the Pinecone MCP or bundled Python scripts) or Assistant (create a Pinecone Assist
AgentDB (external: maintained in ruvnet/agentdb, pinned to commit 27678c3, plugin 3.6.0): agentdb's own standalone MCP server pinned to [email protected] (vector memory with get/list/update/delete for records, patterns and skills, hybrid keyword + vector episode search and an opt-in local cross-encoder, an opt-in aidefence injection guard on memory writes, reflexion, skills, outcome feedback, capabilities report), a memory-specialist agent, 3 skills, /agentdb-status, and the agentdb mod guard that keeps secrets out of memory and refuses delete/reset unless allowed. Complements ruflo-agentdb, which reaches AgentDB through ruflo's server
Query Trackio projects, runs, and metrics using the `trackio` CLI. Use when the user needs to list projects/runs/metrics, get summaries, or retrieve metric values from local Trackio databases. Supports both human-readable and JSON output formats. Covers project discovery, run inspection, metric querying, and system metrics. Designed for LLM agents and automation scripts.
Agent skills for Azure SQL Developer, the Azure SQL Database engine running locally in a container. Teaches your agent to run the engine, connect, migrate, scaffold, build RAG, wire CI, and go local-to-cloud, using the real Private Preview image instead of the SQL Server image.
Plan, build, and debug Zoom integrations across REST APIs, Meeting SDK, Video SDK, webhooks, bots, and MCP workflows. Search meetings, retrieve recordings, access transcripts, and design AI-powered Zoom experiences.
44 skills · 3 MCP servers
anthropics/knowledge-work-plugins 35,601
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