Creating and Managing AI Toolkit Services

pgEdge Cloud databases can be deployed with an installed and configured MCP server, ready for connections. After deployment, use the Services dialog to open the Add MCP Server popup to add AI functionality to an existing cluster or to manage defined functionality.

Adding an MCP Server

Select the Add MCP Server button to access the Add MCP Server popup and define an MCP server, and optionally enable an associated LLM.

Use the fields on the Add MCP Server popup to describe the server and, optionally, the LLM:

• Use the Select Host field to select the cluster host on which this MCP server will be provisioned and run.

• Use the Target Nodes field to optionally select the database nodes this MCP server connects to, in priority order. Defaults to all nodes.

• Use the Allow Writes? toggle to optionally grant the MCP service read-write access (INSERT / UPDATE / DELETE) via the query_database tool. Note that allowing read-write access could potentially expose your data to unexpected or unwanted modifications.

• Use the LLM Enabled? toggle to optionally enable an LLM to generate embeddings for the database. Enabling this activates the generate_embedding tool on the MCP server and requires LLM provider credentials. To enable an LLM, provide the following information:

  • Use the Embedding Provider field to select the provider used by the generate_embedding tool on the MCP server.

  • Use the Embedding Model field to specify the model identifier used by the generate_embedding tool (e.g. text-embedding-3-small, voyage-3).

  • Use the Embedding API Key field to enter the API key for the selected embedding provider. This key is stored encrypted server-side.

When you've defined the MCP server (with optional LLM functionality), select the + Add MCP Server button to update your database.

When the deployment is complete, details about the MCP server deployment are displayed in the Services dialog.

Connecting a Client to the MCP Server

The steps you use to connect a client to the MCP server vary by client and platform. The Services dialog displays connection details for several popular clients under the Connect to MCP Clients label:

Select a tab to view and copy connection details for the client you wish to use. Choose from:

• Claude Code
• Cursor
• OpenAI Codex
• Replit

Adding a RAG Server

Select the Add RAG Server button to access the Add RAG Server popup and define an MCP server, and optionally enable an associated LLM.

Use the fields on the Add MCP Server popup to describe the server:

• Use the Select Host field to select the cluster host on which this RAG server service will be provisioned and run.

• Use the Default Token Budget field to set the maximum number of context tokens (500–128,000) the LLM can process per request.

• Use the Default Top N field to set the number of results retrieved from the vector store before they are trimmed to fit within the token budget.

• Use the Default Embedding LLM Provider field to select the provider whose model will generate vector embeddings for queries and documents during retrieval.

• Use the Default Embedding LLM Model field to specify the embedding model to use. This must match the model used to generate any pre-existing embeddings in your dataset.

• Use the Default Embedding LLM API Key field to enter the API key for authenticating with the selected embedding provider.

• Use the Default Completion LLM Provider field to select the provider whose model will be used for answer generation after relevant documents are retrieved.

• Use the Default Completion LLM Model field to specify the model used for answer generation. You can select a suggested model or enter your own.

• Use the Default Completion LLM API Key field to enter the API key for authenticating with the selected completion provider.

• Use the Add Pipelines field to define one or more named pipelines, each with its own document tables and the option to override the default settings above. Each pipeline is accessible via /v1/pipelines/<name>/search.