Can NoClick create and manage Pinecone indexes automatically?

Yes. The node can create a standard index, create an index for a specific embedding model, configure an existing index's settings like deletion protection and replicas, and delete an index. You can list and describe indexes too, so a workflow can check what exists before it writes.

How do I connect Pinecone to NoClick?

The node authenticates with a Pinecone API key, which you generate in the Pinecone console under API Keys. Paste it into the node's credential field once and every operation in the workflow reuses it.

Can it run semantic search and improve the results?

It can. Use Query / Search for vector similarity search, or Search Records for text-native queries where Pinecone handles the embedding. You can then run Rerank Documents to reorder the matches by relevance before passing them downstream, which is useful for feeding a language model.

Does the node generate embeddings, or do I need a separate step?

Pinecone can do it for you. The node has a Generate Embeddings operation and an Upload and Index Document operation that embeds and stores content in one call, so you do not always need a separate embedding node before writing to an index.

Can a workflow react to changes in Pinecone?

The Pinecone node is request and response, so it does not fire triggers on its own. You drive it from other triggers in the workflow, like a schedule, a form submission, or a new file from another node, and it runs the index or query operation you configured in response.

Hermes is an open agent based on Nous Research’s Hermes models. NoClick runs it for you and adds your connected apps as tools.

How do I give Hermes access to an app?

Connect the app's integration node to the agent's tools handle and pick the operations to expose. Each becomes a callable tool.

Do I need my own infrastructure?

No. The harness runs in a hosted sandbox and NoClick handles credentials for the apps you connect.

Can Hermes use multiple apps at once?

Yes. Wire in several integrations and the agent can use them together in a single run.

Connect Hermes to Pinecone

Give an AI agent your Pinecone as a tool and it can build and query a vector store on its own instead of just reasoning about one. It can create an index, generate embeddings, upload and index documents, run a similarity query, and rerank the matches to surface the best passages, calling each operation as a tool while it works a task. That turns an agent into something that maintains its own retrieval layer, writing new records and reading them back mid-task.

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Opens a workflow with 22 tools ready to use.

What happens next

Set up in minutes

The open button drops you into a guided setup. It asks exactly this, nothing else:

Choose where it runs
On your computer through your own Hermes, or hosted in the cloud. The rest of setup adapts to your choice:
Pick what it can do in Pinecone
A preset of real Pinecone operations. Each one becomes a tool the agent can call, and it can't touch anything outside the list.
Connect Pinecone
One sign-in. The agent acts through your account, scoped to the operations you picked.
Add your personal MCP link
The last step mints a private MCP URL for this agent and walks you through exactly this, with your real link filled in. The page flips to Connected the moment your Hermes first calls in.
1. Run this in your terminal
$hermes mcp add noclick --url https://mcp.noclick.app/s/<your-link>
2. Answer no when asked if the server requires authentication — the link itself is the key
<your-link> is your personal server URL — setup mints it for you.

Exact capabilities

Tools Hermes gets

Pick the operations you want and each becomes a tool the agent can call directly while it works.

Pinecone

22 tools

Query / Search

Find the most similar vectors to a query vector (similarity search).

Upsert Vectors

Insert or overwrite vectors in an index.

Fetch Vectors

Fetch vectors by their IDs.

Delete Vectors

Delete vectors by ID, by metadata filter, or clear a namespace.

Update Vector

Partially update values or metadata on a single existing vector.

List Vectors

List vector IDs in a namespace by prefix (serverless indexes only).

Describe Index Stats

Get per-namespace vector counts, total dimension, and index fullness.

List Indexes

List all indexes in the project.

Describe Index

Get full details for a single index: status, host, metric, dimension, spec.

Create Index

Create a new serverless index.

Configure Index

Change deletion protection or pod replica/type on an existing index.

Delete Index

Delete an index and all its vectors permanently.

Create Index for Model

Create a text-native index that embeds content server-side (no dimension needed).

Upsert Records (Text-Native)

Upsert text records into a text-native index (server-side embedding, no vectors needed).

Search Records (Text-Native)

Search a text-native index with a text query (server-side embedding + optional rerank).

Generate Embeddings

Generate dense embeddings using Pinecone's hosted models.

Rerank Documents

Rerank a set of documents against a query using Pinecone's reranking model.

Start Import

Start an async bulk import of Parquet vectors from S3, GCS, or Azure Blob.

List Imports

List all bulk import operations for an index.

Describe Import

Get the status and details of a specific bulk import operation.

Cancel Import

Cancel a pending or in-progress bulk import operation.

Upload & Index Document

Upload a document file, chunk + embed it with text-embedding-3-small, and upsert.

Starting points

What you can build

When a new document lands from an upstream node, generate embeddings and upload it into a Pinecone index so it becomes searchable right away.

Answer a user's question by running a Query / Search against an index, then rerank the matches to feed only the most relevant passages into an AI step.

On a schedule, describe index stats to check vector counts and alert the team when an index drifts from its expected size.

Keep records current by fetching a vector, updating its values or metadata, and writing the change back without rebuilding the whole index.

Load a large dataset by starting a bulk import, then list and describe imports to track progress until it finishes.

Prune stale content by deleting specific vectors from an index whenever the source records are removed elsewhere in the workflow.

Meet the runner

About Hermes

Hermes is an open agent built on Nous Research’s Hermes models. NoClick runs it hosted and connects your apps to it as tools, so it can do real work across your systems. Wire an integration into the agent and Hermes can use that app’s operations directly while it reasons through a task. It is a strong pick when you want an open-model agent with genuine tool access.

Built on open Hermes models

Capable general reasoning

Flexible, open-model foundation

Hosted with apps wired in as tools

Good to know