Skip the install. Get this working in under 2 minutes.
Start a free trial on cloud.anythingmcp.com, add the Pinecone in one click, then point your AI client (Claude, ChatGPT, Copilot or Cursor) at the generated MCP endpoint. No Docker, no git clone, zero engineering experience required.
Summary
Let your AI search your Pinecone vector database: list indexes, run semantic and keyword search, query and fetch records, check namespaces and stats, embed and rerank with Pinecone Inference, chat with your Pinecone Assistants and, with your confirmation, add records.
Try asking
Example prompts for Pinecone
Click any prompt to copy it. Paste into Claude, ChatGPT, Cursor, Gemini, Copilot or OpenClaw to run it against this connector.
Claude is AI and can make mistakes. Please double-check responses.
💡 No install? Use cloud.anythingmcp.com directly. Sign in, click Connectors → Pinecone, paste your credentials, mint an MCP API key and you're done. No Docker, no
git clone, no local server.
Pinecone + ChatGPT
Let your AI search your Pinecone vector database: list indexes, run semantic and keyword search, query and fetch records, check namespaces and stats, embed and rerank with Pinecone Inference, chat with your Pinecone Assistants and, with your confirmation, add records.
Prerequisites
See the full setup instructions baked into the connector (visible in the in-app store when you select the connector). The required environment variables for this connector are:
PINECONE_API_KEY
Step 1 — Get credentials
- Open the Pinecone console at
https://app.pinecone.io, pick your project, open API keys and click + API key. - Choose the permissions. For read-only use pick ProjectViewer (or ControlPlaneViewer plus DataPlaneViewer): search, embedding, reranking and assistant chat all work with it. Give the key an editor role (ProjectEditor or DataPlaneEditor) only if the AI may write records. On the Starter and Builder plans Pinecone only offers All.
- Copy the key into
PINECONE_API_KEYand install the connector. Pinecone shows it only once, and it usually starts withpcsk_. One key covers one project, and the connector sends it only to pinecone.io. - Check the setup with
pinecone_list_indexes: it lists your indexes with their status and theindex_hostthe data tools need.
Index hosts: every index has its own address. The AI first calls pinecone_list_indexes or pinecone_describe_index and passes the index_host it gets to every search, query and write. You never have to look it up yourself.
The right search for each index: the index schema decides which tool fits.
- Integrated index (created with a hosted embedding model):
pinecone_search_recordssearches with plain text. - Document index (the newer type with typed fields):
pinecone_search_documentscombines keyword search and semantic search. - Index that stores your own vectors:
pinecone_query_vectorsfinds records similar to a stored record, or to a vector thatpinecone_embedcreates with the same model as the index.
pinecone_fetch_vectors and pinecone_list_vector_ids read records by id; pinecone_describe_index_stats and pinecone_list_namespaces count them. Vector values never reach the AI, only ids, scores and metadata.
Pinecone Inference and Assistant: pinecone_list_models, pinecone_embed and pinecone_rerank use the models Pinecone hosts, for example to put search hits in order of relevance. pinecone_list_assistants and pinecone_chat_assistant let the AI ask one of your Pinecone Assistants a question; the answer comes from the files uploaded to it, with citations, and can take 10 to 30 seconds.
Writes ask first: pinecone_upsert_records adds or replaces text records in an integrated index (up to 96 per call), and pinecone_update_metadata changes the metadata of one record. Before each write the AI shows you the index, the namespace and the records or fields and waits for your yes. New records turn up in searches a few seconds later.
Switched off at install: pinecone_delete_vectors, because deleted records cannot be restored. An administrator turns it on deliberately; even then the AI shows you the ids and asks before it deletes.
Costs: searches, queries and fetches use read units and writes use write units of your Pinecone plan; embedding, reranking and assistant chat are billed per token or per request. The AI keeps the number of results small unless you ask for more.
Common errors: 401 (Invalid API key or Wrong API key): the key is wrong, deleted or belongs to another project. 403: the key's role does not allow this, for example a viewer key trying to write. 404: unknown index, namespace or assistant; pinecone_list_indexes and pinecone_list_assistants show the right names. 400 on a text search: the index is not an integrated index, so the AI needs the search that fits its type. 429: a rate or plan limit; wait a moment and try again.
The connector does not create, configure or delete indexes, collections or backups, run bulk imports, upload or delete assistant files, or manage projects and API keys.
Step 2 — Install the adapter
curl -fsSL https://raw.githubusercontent.com/HelpCode-ai/anythingmcp/main/docker-compose.quickstart.yml -o docker-compose.yml
printf 'JWT_SECRET=%s\nENCRYPTION_KEY=%s\n' "$(openssl rand -hex 32)" "$(openssl rand -hex 32)" > .env
docker compose up -d
Step 3 — Add the connector in ChatGPT
Your server URL: in AnythingMCP, open MCP Servers → the server this connector is on, and copy its URL (
https://cloud.anythingmcp.com/mcp/…). Use it wherever this guide showsYOUR_SERVER_ID.
ChatGPT adds MCP servers as plugins on its Plugins page, on the web and on paid plans. Developer mode is no longer needed. In a Business workspace only admins and owners can add one; in Enterprise and Edu an admin can grant the Create plugins with MCPs permission.
- Go to chatgpt.com/plugins, click +, then Add custom MCP server.
- Enter a name, e.g. Name:
Pinecone. - Under Connection, enter the Server URL
https://cloud.anythingmcp.com/mcp/YOUR_SERVER_IDand keep Authentication on OAuth. - Select I understand and want to continue, then Create as a plugin. When ChatGPT asks, sign in to AnythingMCP and allow access.
- Install the new plugin from your personal plugins, then type @ in a chat and pick it.
Available tools
| Tool | What it does |
|---|---|
pinecone_list_indexes | List the indexes of the Pinecone project: name, index_host (pass it to every data tool), readiness, deployment (serverless cloud and… |
pinecone_describe_index | Describe one index: index_host (pass it to the data tools), readiness, deployment, schema (dimension, metric, embedding model and field map… |
pinecone_describe_index_stats | Get statistics of an index: total record count, record count per namespace, dimension, metric, vector type and fullness (pod indexes) |
pinecone_list_namespaces | List the namespaces of an index with their record counts, 100 per page; call again with pagination_token = pagination.next until it is… |
pinecone_search_records | Semantic search with plain text on an integrated index (one created with a hosted embedding model; its schema shows a semantic_text field)… |
pinecone_search_documents | Search a document index (the newer index type with a typed schema) with one or more scoring methods: full-text (type text or query_string)… |
pinecone_query_vectors | Find the records most similar to a stored record (by id) or to a query vector you pass, optionally with a metadata filter |
pinecone_fetch_vectors | Fetch stored records by id: id and metadata of each |
pinecone_list_vector_ids | List the record ids in a namespace (serverless indexes), optionally only those starting with a prefix such as doc1#; up to 100 per page… |
pinecone_upsert_records | Add or replace text records in an integrated index; Pinecone embeds the text with the index's model |
pinecone_update_metadata | Set metadata fields on one stored record; fields not named stay as they are and the vector is unchanged |
pinecone_delete_vectors | Delete stored records by id |
pinecone_list_models | List the embedding and reranking models Pinecone hosts, with type, vector type, dimensions, maximum input and batch size and supported… |
pinecone_embed | Turn texts into vectors with a Pinecone-hosted embedding model, e.g. to query an index that stores your own vectors with… |
pinecone_rerank | Reorder documents by relevance to a query with a Pinecone-hosted reranking model: returns each document's index in the input list and a… |
pinecone_list_assistants | List the Pinecone Assistants of the project: name, status, instructions, metadata, host and region, and when they were created |
pinecone_chat_assistant | Ask a Pinecone Assistant a question: it answers from the files uploaded to it and returns the answer with citations (file name and pages) |
Example prompts
- "Which indexes do we have in Pinecone, and how many records does each namespace hold?"
- "Search the help-center index for how to cancel a subscription and show me the five best matches with their scores."
- "Ask our Pinecone Assistant contracts-bot what the notice period in the Acme agreement is, and show the citations."
- "Rerank these six passages by how well they answer the question about refunds on annual plans, best first."
- "Add these three FAQ answers as records to the faq namespace of the help-center index. Show me the records before you write them."
FAQ
Does the free ChatGPT plan work with custom MCP servers? OpenAI documents custom MCP servers for paid plans only; the Free plan is not on its list.
Next steps
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