Skip to main content
The Bing Search connector exposes web search as a remote MCP server for AI agents. It uses Bing Grounding through Azure AI Foundry and returns sourced search results in the standard MCP content response format.

Current Web Search

Retrieve recent information from the web with source URLs

Azure Foundry

Uses Azure AI Foundry Assistants with a configured Bing Grounding connection

MCP Native

Exposes a JSON-RPC MCP endpoint that AI Knowledge agents can call directly

​
Prerequisites

  • An Azure subscription with access to Azure AI Foundry
  • An Azure AI Foundry project with a Bing Grounding connection
  • An Azure AD application using the client credentials flow
  • Workspace secrets for the Azure tenant, client ID, client secret, and token scope
This connector is designed for agent web-search scenarios and exposes the bing_search MCP tool.

​
Instance Configuration

The Bing Web Search (Azure Foundry) connector is available in your Prisme.ai instance. Before using it from an agent:
  1. Open the connector configuration
  2. Verify the Azure Foundry project settings in the connector configuration
  3. Set the required Azure credentials in workspace secrets

​
Azure App Registration

The connector authenticates to Azure AI Foundry with Microsoft Entra ID client credentials. You can create a dedicated app registration or reuse an existing service principal.
  1. In Microsoft Entra ID, create an App registration
  2. Copy the Directory (tenant) ID and Application (client) ID
  3. Create a client secret in Certificates & secrets
  4. Assign the service principal the Azure AI User role on the Azure AI Foundry resource or project used by the connector
  5. Make sure the Azure AI Foundry project has access to the Bing Grounding connection configured below
Unlike Microsoft Graph connectors such as Outlook, SharePoint, or Excel, this connector does not require Graph API permissions or Graph admin consent. Access is controlled through Azure AI Foundry RBAC and the configured Bing Grounding connection.

​
Connector Configuration

Set these values in the connector configuration. They define which Azure Foundry project, model, and Bing Grounding connection the connector uses.
FieldDescription
Providerbing
Foundry Base URLAzure AI Foundry service URL, for example https://<resource>.services.ai.azure.com
Foundry ProjectAzure AI Foundry project name
Foundry API VersionAzure AI Foundry Assistants API version
Foundry ModelModel used by the temporary search assistant
Bing Connection IDFull Azure resource ID of the Bing Grounding connection
Default search options can also be configured:
FieldDefaultDescription
Default Count5Number of search results when num_results is omitted
Default Marketen-USMarket code when market is omitted
Default FreshnessWeekRecency filter when freshness is omitted
Token Cache TTL3300Azure access token cache duration in seconds

​
Workspace Secrets

Set these values in the workspace secrets panel. They are referenced by the connector configuration and are used to request Azure AD access tokens with the client credentials flow.
SecretDescription
AZURE_TENANT_IDAzure AD directory tenant ID
AZURE_CLIENT_IDAzure AD application client ID
AZURE_CLIENT_SECRETAzure AD application client secret
AZURE_TOKEN_SCOPEOAuth scope used when requesting the Azure access token, usually https://ai.azure.com/.default
Keep Azure credentials in workspace secrets. Do not paste client secrets into the visible connector configuration fields.

​
MCP Setup

  1. Open your AI Knowledge project
  2. Go to Advanced > Tools
  3. Click Add and select the MCP tab
  4. Paste the MCP endpoint URL from the connector configuration
  5. Save the tool and let the agent discover the bing_search tool

​
Available Tool

ToolDescription
bing_searchSearch the web through Bing Grounding and return sourced results

​
Tool Arguments

ArgumentTypeRequiredDescription
querystringYesSearch query
num_resultsnumberNoNumber of results to request, from 1 to 10
marketstringNoMarket code, for example en-US or fr-FR
freshnessstringNoRecency filter: Day, Week, or Month

​
MCP Examples

​
Search with Defaults

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "bing_search",
    "arguments": {
      "query": "latest Azure AI Foundry Bing Grounding documentation"
    }
  }
}

​
Search a French Market with Fresh Results

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "bing_search",
    "arguments": {
      "query": "actualites intelligence artificielle entreprise",
      "num_results": 5,
      "market": "fr-FR",
      "freshness": "Day"
    }
  }
}

​
Response Format

Successful calls return an MCP tool result: 
{
  "content": [
    {
      "type": "text",
      "text": "Search results with titles, URLs, snippets, and source annotations"
    }
  ],
  "isError": false
}
If the query is missing or Azure authentication fails, the connector returns isError: true with a text error message.

​
Troubleshooting

Error: query parameter is required - The MCP call did not include params.arguments.query. Authentication error - Check the Azure tenant ID, client ID, client secret, and token scope secrets in the connector workspace. Failed to create assistant - Verify the Foundry base URL, project name, API version, model, and Bing Grounding connection ID. Search did not complete - The Azure Foundry assistant run did not finish before the connector timeout. Retry with a narrower query or fewer requested results.

Connectors Overview

Compare App and MCP connector usage modes

AI Knowledge Tools

Add MCP tools to an AI Knowledge agent