Jentic MCP connector
OAuth 2.1/DCR Developer ToolsAutomationAIConnect to Jentic MCP. Search available API actions, load execution details, manage credentials, and execute API operations from your AI workflows.
Jentic MCP connector
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Install the SDK
Section titled “Install the SDK”Terminal window npm install @scalekit-sdk/nodeTerminal window pip install scalekit -
Set your credentials
Section titled “Set your credentials”Add your Scalekit credentials to your
.envfile. Find values in app.scalekit.com > Developers > API Credentials..env SCALEKIT_ENVIRONMENT_URL=<your-environment-url>SCALEKIT_CLIENT_ID=<your-client-id>SCALEKIT_CLIENT_SECRET=<your-client-secret> -
Authorize and make your first call
Section titled “Authorize and make your first call”quickstart.ts import { ScalekitClient } from '@scalekit-sdk/node'import 'dotenv/config'const scalekit = new ScalekitClient(process.env.SCALEKIT_ENV_URL,process.env.SCALEKIT_CLIENT_ID,process.env.SCALEKIT_CLIENT_SECRET,)const actions = scalekit.actionsconst connector = 'jenticmcp'const identifier = 'user_123'// Generate an authorization link for the userconst { link } = await actions.getAuthorizationLink({ connectionName: connector, identifier })console.log('Authorize Jentic MCP:', link)process.stdout.write('Press Enter after authorizing...')await new Promise(r => process.stdin.once('data', r))// Make your first callconst result = await actions.executeTool({connector,identifier,toolName: 'jenticmcp_list_credentials',toolInput: {},})console.log(result)quickstart.py import osfrom scalekit.client import ScalekitClientfrom dotenv import load_dotenvload_dotenv()scalekit_client = ScalekitClient(env_url=os.getenv("SCALEKIT_ENV_URL"),client_id=os.getenv("SCALEKIT_CLIENT_ID"),client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"),)actions = scalekit_client.actionsconnection_name = "jenticmcp"identifier = "user_123"# Generate an authorization link for the userlink_response = actions.get_authorization_link(connection_name=connection_name,identifier=identifier,)print("Authorize Jentic MCP:", link_response.link)input("Press Enter after authorizing...")# Make your first callresult = actions.execute_tool(tool_input={},tool_name="jenticmcp_list_credentials",connection_name=connection_name,identifier=identifier,)print(result)
What you can do
Section titled “What you can do”Connect this agent connector to let your agent:
- Search apis — Search for available API actions based on a natural language description of what the user wants to do
- Info load execution — Retrieve detailed information about a specific action before running it, including required inputs and parameters
- List credentials — List all API credentials the authenticated agent has access to, showing which APIs are available to use
- Execute records — Execute a specific API action using provided parameters, including any required inputs for the operation
Tool list
Section titled “Tool list”Use the exact tool names from the Tool list below when you call execute_tool. If you’re not sure which name to use, list the tools available for the current user first.
jenticmcp_execute
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Execute a specific API action using provided parameters, including any required inputs for the operation. 1 param
Execute a specific API action using provided parameters, including any required inputs for the operation.
params object required No description. jenticmcp_list_credentials
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List all API credentials the authenticated agent has access to, showing which APIs are available to use. 0 params
List all API credentials the authenticated agent has access to, showing which APIs are available to use.
jenticmcp_load_execution_info
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Retrieve detailed information about a specific action before running it, including required inputs and parameters. 1 param
Retrieve detailed information about a specific action before running it, including required inputs and parameters.
request object required No description. jenticmcp_search_apis
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Search for available API actions based on a natural language description of what the user wants to do. 1 param
Search for available API actions based on a natural language description of what the user wants to do.
request object required No description.