pinecone: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, CI/CD automation, and full control over Pinecone resources.
What this skill does
# Pinecone CLI (`pc`)
Manage Pinecone from the terminal. The CLI is especially valuable for vector operations across **all index types** — something the MCP currently can't do.
## CLI vs MCP
| | CLI | MCP |
|---|---|---|
| Index types | All (standard, integrated, sparse) | Integrated only |
| Vector ops (upsert, query, fetch, update, delete) | ✅ | ❌ |
| Text search on integrated indexes | ✅ | ✅ |
| Backups, namespaces, org/project mgmt | ✅ | ❌ |
| CI/CD / scripting | ✅ | ❌ |
---
## Setup
### Install (macOS)
```bash
brew tap pinecone-io/tap
brew install pinecone-io/tap/pinecone
```
Other platforms (Linux, Windows) — download from [GitHub Releases](https://github.com/pinecone-io/cli/releases).
### Authenticate
```bash
# Interactive (recommended for local dev)
pc login
pc target -o "my-org" -p "my-project"
# Service account (recommended for CI/CD)
pc auth configure --client-id "$PINECONE_CLIENT_ID" --client-secret "$PINECONE_CLIENT_SECRET"
# API key (quick testing)
pc config set-api-key $PINECONE_API_KEY
```
Check status: `pc auth status` · `pc target --show`
> **Note for agent sessions**: If you need to run `pc login` inside an agent loop, the browser auth link may not surface correctly. It's best to authenticate **before** starting an agent session. Run `pc login` in your terminal directly, then invoke the agent once you're authenticated.
### Authenticating the CLI does not set `PINECONE_API_KEY`
`pc login` authenticates the CLI tool itself — it does **not** set `PINECONE_API_KEY` in your environment. Python scripts, Node.js SDKs, and other tools that use the Pinecone SDK need `PINECONE_API_KEY` set separately.
Use the CLI to create a key and export it in one step:
```bash
KEY=$(pc api-key create --name agent-sdk-key --json | jq -r '.value')
export PINECONE_API_KEY="$KEY"
```
Without `jq`: run `pc api-key create --name agent-sdk-key --json` and copy the `"value"` field manually.
---
## Common Commands
| Task | Command |
|---|---|
| List indexes | `pc index list` |
| Create serverless index | `pc index create -n my-index -d 1536 -m cosine -c aws -r us-east-1` |
| Index stats | `pc index stats -n my-index` |
| Upload vectors from file | `pc index vector upsert -n my-index --file ./vectors.json` |
| Query by vector | `pc index vector query -n my-index --vector '[0.1, ...]' -k 10 --include-metadata` |
| Query by vector ID | `pc index vector query -n my-index --id "doc-123" -k 10` |
| Fetch vectors by ID | `pc index vector fetch -n my-index --ids '["vec1","vec2"]'` |
| List vector IDs | `pc index vector list -n my-index` |
| Delete vectors by filter | `pc index vector delete -n my-index --filter '{"genre":"classical"}'` |
| List namespaces | `pc index namespace list -n my-index` |
| Create backup | `pc backup create -i my-index -n "my-backup"` |
| JSON output (for scripting) | Add `-j` to any command |
---
## Interesting Things You Can Do
### Query with custom vectors (not just text)
Unlike the MCP, the CLI lets you query any index with raw vector values — useful when you generate embeddings externally (OpenAI, HuggingFace, etc.):
```bash
pc index vector query -n my-index \
--vector '[0.1, 0.2, ..., 0.9]' \
--filter '{"source":{"$eq":"docs"}}' \
-k 20 --include-metadata
```
### Pipe embeddings directly into queries
```bash
jq -c '.embedding' doc.json | pc index vector query -n my-index --vector - -k 10
```
### Bulk metadata update with preview
```bash
# Preview first
pc index vector update -n my-index \
--filter '{"env":{"$eq":"staging"}}' \
--metadata '{"env":"production"}' \
--dry-run
# Apply
pc index vector update -n my-index \
--filter '{"env":{"$eq":"staging"}}' \
--metadata '{"env":"production"}'
```
### Backup and restore
```bash
# Snapshot before a migration
pc backup create -i my-index -n "pre-migration"
# Restore to a new index if something goes wrong
pc backup restore -i <backup-uuid> -n my-index-restored
```
### Automate in CI/CD
```bash
export PINECONE_CLIENT_ID="..."
export PINECONE_CLIENT_SECRET="..."
pc auth configure --client-id "$PINECONE_CLIENT_ID" --client-secret "$PINECONE_CLIENT_SECRET"
pc index vector upsert -n my-index --file ./vectors.jsonl --batch-size 1000
```
### Script against JSON output
```bash
# Get all index names as a list
pc index list -j | jq -r '.[] | .name'
# Check if an index exists before creating
if ! pc index describe -n my-index -j 2>/dev/null | jq -e '.name' > /dev/null; then
pc index create -n my-index -d 1536 -m cosine -c aws -r us-east-1
fi
```
---
## Reference Files
- [Full command reference](references/command-reference.md) — all commands with flags and examples
- [Troubleshooting & best practices](references/troubleshooting.md)
## Documentation
- [CLI Quickstart](https://docs.pinecone.io/reference/cli/quickstart)
- [Command Reference](https://docs.pinecone.io/reference/cli/command-reference)
- [Authentication](https://docs.pinecone.io/reference/cli/authentication)
- [Target Context](https://docs.pinecone.io/reference/cli/target-context)
- [GitHub Releases](https://github.com/pinecone-io/cli/releases)
Related in Cloud & DevOps
appbuilder-action-scaffolder
IncludedCreate, implement, deploy, and debug Adobe Runtime actions with consistent layout, validation, and error handling. Use this skill whenever the user needs to add actions to an App Builder project, understand action structure (params, response format, web/raw actions), configure actions in the manifest, use App Builder SDKs (State, Files, Events, database), deploy and invoke actions via CLI, debug action issues, or implement patterns such as webhook receivers, custom event providers, journaling consumers, large payload redirects, action sequence pipelines, and Asset Compute workers. Also trigger when users mention serverless functions in Adobe context, action logging, IMS authentication for actions, or cron-style scheduled actions.
orchestrating-datacloud
IncludedSalesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the user needs a multi-step Data Cloud pipeline, cross-phase troubleshooting, or data space and data kit management. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase sf data360 workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching phase-specific skill), the task is STDM/session tracing/parquet telemetry (use observing-agentforce), standard CRM SOQL (use querying-soql), or Apex implementation (use generating-apex).
github-project-automation
IncludedAutomate GitHub repository setup with CI/CD workflows, issue templates, Dependabot, and CodeQL security scanning. Includes 12 production-tested workflows and prevents 18 errors: YAML syntax, action pinning, and configuration. Use when: setting up GitHub Actions CI/CD, creating issue/PR templates, enabling Dependabot or CodeQL scanning, deploying to Cloudflare Workers, implementing matrix testing, or troubleshooting YAML indentation, action version pinning, secrets syntax, runner versions, or CodeQL configuration. Keywords: github actions, github workflow, ci/cd, issue templates, pull request templates, dependabot, codeql, security scanning, yaml syntax, github automation, repository setup, workflow templates, github actions matrix, secrets management, branch protection, codeowners, github projects, continuous integration, continuous deployment, workflow syntax error, action version pinning, runner version, github context, yaml indentation error
sf-datacloud
IncludedSalesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase `sf data360` workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching sf-datacloud-* skill), the task is STDM/session tracing/parquet telemetry (use sf-ai-agentforce-observability), standard CRM SOQL (use sf-soql), or Apex implementation (use sf-apex).
fabric-cli
IncludedUse this skill for Fabric.so CLI workflows with the `fabric` terminal command: diagnose/install/login, search or browse a Fabric library, save notes/links/files, create folders, ask the Fabric AI assistant, manage tasks/workspaces, generate shell completion, check subscription usage, produce JSON output, and use Fabric as persistent agent memory. Do not use for Microsoft Fabric/Azure/Power BI `fab`, Daniel Miessler's Fabric framework, Python Fabric SSH, Fabric.js, or textile/fashion fabric.
lark
IncludedLark/Feishu CLI skills: lark-cli operations for docs, markdown, sheets, base, calendar, im, mail, task, okr, drive, wiki, slides, whiteboard, apps, approval, attendance, contact, vc, minutes, event. Use when the user needs to operate Lark/Feishu resources via lark-cli, send messages, manage documents, spreadsheets, calendars, tasks, OKRs, deploy web pages, or any Feishu/Lark workspace operations.