{ "checker": { "claims": [ { "claim": "The DataRobot-compatible MCP server runs and its drtools docs search tool returns real results", "command": " tools/call {\"tool\": \"search_datarobot_agentic_docs\", \"arguments\": {\"query\": \"MCP server setup\", \"max_results\": 2}}", "evidence": "\u2026MCP: DataRobot docs\\\",\\\"result_0_url\\\":\\\"https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/index.html\\\",\\\"result_0_description\\\":\\\"Release notes for September are available MCP \u00b6 Premium DataRobot's Agentic AI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. The Model Context Protocol (MCP) provides a standardized interface for AI agents to interact with external systems, tools, and data sources. Use MCP to centralize tool management, integra", "expected": "search results including MCP: DataRobot docs", "input": "{\"tool\": \"search_datarobot_agentic_docs\", \"arguments\": {\"query\": \"MCP server setup\", \"max_results\": 2}}", "kind": "claim", "outcome": "Real input produced the expected result: MCP: DataRobot docs", "source": "Library of agentic tools and DataRobot-compatible MCP server", "source_checked": true, "source_file": "README.md", "verdict": "yes" } ], "cycle_checks": [ { "claim": "Scheduled or looping main behavior", "command": "", "evidence": "Inspected README.md and src/datarobot_genai (grep for cron, --once, --now, schedule loops, webhooks): no product scheduler, cron, loop, or webhook trigger exists; only CI schedules and the Taskfile dev entrypoint were found.", "expected": "identify the product trigger", "input": "source inspection", "kind": "cycle", "outcome": "Inspected README.md and src/datarobot_genai (grep for cron, --once, --now, schedule loops, webhooks): no product scheduler, cron, loop, or webhook trigger exists; only CI schedules and the Taskfile dev entrypoint were found.", "source": "repository entry points", "verdict": "yes" } ], "details": { "argv": [ "", "" ], "hosting": "local", "kind": "mcp_http", "protocolVersion": "2025-06-18", "scope": "remote hosted MCP initialize + tools/list", "serverInfo": { "name": "datarobot-mcp-server", "version": "3.4.11" }, "tool_result": { "content": [ { "text": "{\"status\":\"success\",\"query\":\"MCP server setup\",\"total_results\":2,\"result_0_title\":\"MCP: DataRobot docs\",\"result_0_url\":\"https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/index.html\",\"result_0_description\":\"Release notes for September are available MCP \u00b6 Premium DataRobot's Agentic AI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. The Model Context Protocol (MCP) provides a standardized interface for AI agents to interact with external systems, tools, and data sources. Use MCP to centralize tool management, integrate tools into agentic workflows, and connect IDEs and chat clients to your DataRobot MCP server. Topic Description MCP server overview Learn what MCP servers are and compare the DataRobot Global MCP, standalone MCP template, and Agentic Starter bundled server. Integrate tools using an MCP server Integrate tools into your agentic workflows using an MCP server for centralized tool management and a standardized interface. Dynamic tool registration Discover DataRobot deployments and expose them as MCP tools automatically through the MCP server. Connect agentic coding environments to MCP servers Configure Cursor, Claude Desktop, and VS Code to use your DataRobot MCP server for tools and resources.\",\"result_1_title\":\"Connect agentic coding environments to MCP servers: DataRobot docs\",\"result_1_url\":\"https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-mcp-clients.html\",\"result_1_description\":\"Release notes for September are available Connect agentic coding environments to MCP servers \u00b6 You can connect your DataRobot MCP server to standard agentic coding environments\u2014such as Cursor, Claude Desktop, and VS Code\u2014to allow AI assistants in those environments to discover and call your MCP tools, prompts, and resources. This enables you to use DataRobot capabilities (e.g., projects, deployments, predictions, third-party tools) directly from your IDE or chat client. This guide explains how to configure each client to use an MCP server that you run locally or that is deployed to DataRobot. It applies to any of the following MCP connection options: An MCP server deployed using the DataRobot Agentic Starter template ( datarobot-agent-application ). A standalone MCP server deployed using the DataRobot MCP template ( af-component-datarobot-mcp ). An MCP server implemented by the DataRobot Global MCP. MCP server vs. agent application This page focuses on connecting MCP clients (Cursor, Claude Desktop, VS Code) to an MCP server. For integrating an MCP server into a DataRobot agentic workflow (e.g., LangGraph agent in the Agentic Starter template), see Integrate tools using an MCP server . Using the DataRobot Global MCP \u00b6 The DataRobot Global MCP is a service automatically deployed to your DataRobot instance that agentic workflows can use to access tools. Available tools The DataRobot Global MCP currently supports tools for predictive AI. This limitation will be removed in a future release. Configuration \u00b6 The DataRobot Global MCP requires an API key to authenticate requests. You can obtain your API key from the DataRobot UI by opening the user menu and selecting API keys and tools . See API key management for more information. Once you have your API key, configure the MCP client to use the DataRobot Global MCP endpoint. Refer to the steps that correspond to your MCP client in the tabs below. To verify the connection, save .cursor/mcp.json in the correct location, restart Cursor or reload the window, then in Chat or Composer ask the AI to list or use tools from the DataRobot MCP server. To verify the connection, save claude_desktop_config.json in the correct location, restart Claude Desktop, then ask Claude to use tools from the DataRobot MCP server. To verify the connection, save .vscode/mcp.json under .vscode/ or your user profile ( MCP: Open User Configuration in the Command Palette), reload the window if prompted, then in Copilot Chat ask the AI to list or use tools from the DataRobot MCP server. Using a standalone MCP server \u00b6 You can also use a standalone MCP server that you deploy to your own infrastructure. To use a standalone MCP server, you need to configure your MCP client to use the standalone MCP server endpoint. Prerequisites \u00b6 Before configuring your coding environment, ensure that you have: A running MCP server (local or deployed): Local An MCP server started via the DataRobot Agentic Starter template (default port 9000 ) or the DataRobot MCP template (default port 8080 ). Base URL (e.g., http://localhost:9000/mcp or http://localhost:8080/mcp ). An MCP server started via the DataRobot Agentic Starter template (default port 9000 ) or the DataRobot MCP template (default port 8080 ). Base URL (e.g., http://localhost:9000/mcp or http://localhost:8080/mcp ). Deployed MCP server deployed to DataRobot; you will need the MCP endpoint URL (e.g., https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp ). Base URL (e.g., https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp ) MCP server deployed to DataRobot; you will need the MCP endpoint URL (e.g., https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp ). Base URL (e.g., https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp ) Endpoint and auth \\\"Authorization\\\": \\\" \\\" (required for authentication with the MCP server) \\\"x-datarobot-api-token\\\": \\\" \\\" (required for tool execution) Finding the deployed MCP endpoint To find the endpoint of the deployed MCP server: For the DataRobot MCP template , after deploying run task infra:info or check the Pulumi/output step for MCP_SERVER_MCP_ENDPOINT . For the Agentic Starter template , the deployment output includes an MCP server endpoint. Use the URL shown there for your client. Endpoint reference \u00b6 Context Base URL Notes Agentic Starter (local) http://localhost:9000/mcp Default port is 9000; set MCP_SERVER_PORT to change it. MCP template (local) http://localhost:8080/mcp Default port is 8080; set MCP_SERVER_PORT to change it. Deployed to DataRobot https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp Use the exact URL from your deployment output. Configure your environment \u00b6 You can configure your MCP client to use the MCP server endpoint. Refer to the steps that correspond to your MCP client in the tabs below. Cursor MCP docs For Cursor's current MCP options, see Cursor's MCP documentation . Configuration file location : Project-specific: /.cursor/mcp.json Global: ~/.cursor/mcp.json For a local MCP server using the DataRobot Agentic Starter on port 9000: For a local MCP server using the DataRobot MCP template on port 8080: For a deployed MCP server: Claude Desktop can connect to remote MCP servers over HTTP. Configure the MCP server in claude_desktop_config.json . Claude Desktop MCP docs For Claude Desktop's current MCP options, see Claude Desktop's MCP documentation . Configuration file location : macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\\\\Claude\\\\claude_desktop_config.json For a local MCP server using the DataRobot Agentic Starter on port 9000: For a local MCP server using the DataRobot MCP template on port 8080: For a deployed MCP server: If your deployment requires authentication, add the appropriate headers or token mechanism as supported by Claude Desktop and your MCP server (e.g., environment variables or config fields; check Claude Desktop documentation and your server docs). Debugging Claude Desktop MCP On macOS, MCP-related logs are often under ~/Library/Logs/Claude/ (e.g., mcp*.log ). Use them to troubleshoot connection or auth issues. VS Code (with GitHub Copilot) includes built-in MCP client support. Configure remote MCP servers in an mcp.json file (HTTP servers use type , url , and optional headers ). VS Code Copilot MCP docs For VS Code's current MCP options, the configuration schema, and security notes, see Use MCP servers in VS Code and the MCP configuration reference . Configuration file location : Workspace: /.vscode/mcp.json (appropriate for shared, non-secret settings that you may commit to source control). User profile: run MCP: Open User Configuration from your coding environment's Command Palette ( \u21e7\u2318P / Ctrl+Shift+P ). Prefer this location, or another secret-management mechanism, for API keys and other credentials. If your MCP configuration includes headers such as Authorization or x-datarobot-api-token , do not commit those secrets to source control. Keep credentialed configuration in your user-profile mcp.json or another secure secret store. You can also use MCP: Add Server in your coding environment's Command Palette for a guided setup. For more options, see Add and manage MCP servers in VS Code . For a local MCP server using the DataRobot Agentic Starter on port 9000: For a local MCP server using the DataRobot MCP template on port 8080: For a deployed MCP server: If your deployed server requires a token, configure authentication using HTTP headers supported by your MCP server. Avoid passing tokens in URLs or query parameters to reduce the risk of credential leakage; see Use MCP servers in VS Code and your server docs. Apply and verify \u00b6 To confirm connectivity: Save your MCP configuration in the correct location. Restart your MCP client (or reload the window). In your MCP client, ask the AI to list or use tools from the DataRobot MCP server. Troubleshooting \u00b6 Client cannot connect to MCP server \u00b6 Symptoms : The IDE or Claude reports that the MCP server is unavailable, or tools do not appear. Solutions : Confirm the MCP server is running: Local: run curl -i http://localhost:9000/ or curl -i http://localhost:8080/ (adjust the port to match your local MCP server setup). A successful response indicates the server is up. Deployed: run curl -i using the exact URL from your deployment output. If the server requires authentication, you may need to pass a bearer token or other headers; see your deployment documentation. A successful response indicates the server is up. Check URL and path: Use the exact base URL and path (e.g., /mcp ) required by your client and server. Confirm firewall and network access: For deployed servers, ensure your network allows outbound HTTPS to the DataRobot host. Tools do not appear in the client \u00b6 Symptoms : Connection seems OK, but the client does not list MCP tools. Solutions : Restart the client (Cursor, Claude Desktop, or VS Code) after changing the MCP config. Check client and MCP server logs for errors (e.g., Cursor: Output \u2192 MCP Logs; Claude: ~/Library/Logs/Claude/ ). A successful connection will show MCP server logs with tool registration and availability information. Authentication errors \u00b6 Symptoms : Requests to the deployed MCP server return 401 or similar. Solutions : Confirm the deployed server's auth requirements (e.g., bearer token). Ensure the client is configured with the correct token or credentials (environment variables or extension settings). For DataRobot deployments, ensure DATAROBOT_API_TOKEN (or the equivalent used by the server) is valid and has access to the deployment. Additional resources \u00b6 Integrate tools using an MCP server \u2014 Use an MCP server from a DataRobot agentic workflow (e.g., LangGraph agent). DataRobot MCP template \u2014 Build and deploy standalone MCP servers with DataRobot integration. DataRobot MCP template \u2014 Configure your MCP client \u2014 Client setup details from the template repo. DataRobot Agentic Starter template \u2014 Full agentic application including an MCP server. Model Context Protocol \u2014 Official MCP specification.\"}", "type": "text" } ], "isError": false, "structuredContent": { "query": "MCP server setup", "result_0_description": "Release notes for September are available MCP \u00b6 Premium DataRobot's Agentic AI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. The Model Context Protocol (MCP) provides a standardized interface for AI agents to interact with external systems, tools, and data sources. Use MCP to centralize tool management, integrate tools into agentic workflows, and connect IDEs and chat clients to your DataRobot MCP server. Topic Description MCP server overview Learn what MCP servers are and compare the DataRobot Global MCP, standalone MCP template, and Agentic Starter bundled server. Integrate tools using an MCP server Integrate tools into your agentic workflows using an MCP server for centralized tool management and a standardized interface. Dynamic tool registration Discover DataRobot deployments and expose them as MCP tools automatically through the MCP server. Connect agentic coding environments to MCP servers Configure Cursor, Claude Desktop, and VS Code to use your DataRobot MCP server for tools and resources.", "result_0_title": "MCP: DataRobot docs", "result_0_url": "https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/index.html", "result_1_description": "Release notes for September are available Connect agentic coding environments to MCP servers \u00b6 You can connect your DataRobot MCP server to standard agentic coding environments\u2014such as Cursor, Claude Desktop, and VS Code\u2014to allow AI assistants in those environments to discover and call your MCP tools, prompts, and resources. This enables you to use DataRobot capabilities (e.g., projects, deployments, predictions, third-party tools) directly from your IDE or chat client. This guide explains how to configure each client to use an MCP server that you run locally or that is deployed to DataRobot. It applies to any of the following MCP connection options: An MCP server deployed using the DataRobot Agentic Starter template ( datarobot-agent-application ). A standalone MCP server deployed using the DataRobot MCP template ( af-component-datarobot-mcp ). An MCP server implemented by the DataRobot Global MCP. MCP server vs. agent application This page focuses on connecting MCP clients (Cursor, Claude Desktop, VS Code) to an MCP server. For integrating an MCP server into a DataRobot agentic workflow (e.g., LangGraph agent in the Agentic Starter template), see Integrate tools using an MCP server . Using the DataRobot Global MCP \u00b6 The DataRobot Global MCP is a service automatically deployed to your DataRobot instance that agentic workflows can use to access tools. Available tools The DataRobot Global MCP currently supports tools for predictive AI. This limitation will be removed in a future release. Configuration \u00b6 The DataRobot Global MCP requires an API key to authenticate requests. You can obtain your API key from the DataRobot UI by opening the user menu and selecting API keys and tools . See API key management for more information. Once you have your API key, configure the MCP client to use the DataRobot Global MCP endpoint. Refer to the steps that correspond to your MCP client in the tabs below. To verify the connection, save .cursor/mcp.json in the correct location, restart Cursor or reload the window, then in Chat or Composer ask the AI to list or use tools from the DataRobot MCP server. To verify the connection, save claude_desktop_config.json in the correct location, restart Claude Desktop, then ask Claude to use tools from the DataRobot MCP server. To verify the connection, save .vscode/mcp.json under .vscode/ or your user profile ( MCP: Open User Configuration in the Command Palette), reload the window if prompted, then in Copilot Chat ask the AI to list or use tools from the DataRobot MCP server. Using a standalone MCP server \u00b6 You can also use a standalone MCP server that you deploy to your own infrastructure. To use a standalone MCP server, you need to configure your MCP client to use the standalone MCP server endpoint. Prerequisites \u00b6 Before configuring your coding environment, ensure that you have: A running MCP server (local or deployed): Local An MCP server started via the DataRobot Agentic Starter template (default port 9000 ) or the DataRobot MCP template (default port 8080 ). Base URL (e.g., http://localhost:9000/mcp or http://localhost:8080/mcp ). An MCP server started via the DataRobot Agentic Starter template (default port 9000 ) or the DataRobot MCP template (default port 8080 ). Base URL (e.g., http://localhost:9000/mcp or http://localhost:8080/mcp ). Deployed MCP server deployed to DataRobot; you will need the MCP endpoint URL (e.g., https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp ). Base URL (e.g., https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp ) MCP server deployed to DataRobot; you will need the MCP endpoint URL (e.g., https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp ). Base URL (e.g., https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp ) Endpoint and auth \"Authorization\": \" \" (required for authentication with the MCP server) \"x-datarobot-api-token\": \" \" (required for tool execution) Finding the deployed MCP endpoint To find the endpoint of the deployed MCP server: For the DataRobot MCP template , after deploying run task infra:info or check the Pulumi/output step for MCP_SERVER_MCP_ENDPOINT . For the Agentic Starter template , the deployment output includes an MCP server endpoint. Use the URL shown there for your client. Endpoint reference \u00b6 Context Base URL Notes Agentic Starter (local) http://localhost:9000/mcp Default port is 9000; set MCP_SERVER_PORT to change it. MCP template (local) http://localhost:8080/mcp Default port is 8080; set MCP_SERVER_PORT to change it. Deployed to DataRobot https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp Use the exact URL from your deployment output. Configure your environment \u00b6 You can configure your MCP client to use the MCP server endpoint. Refer to the steps that correspond to your MCP client in the tabs below. Cursor MCP docs For Cursor's current MCP options, see Cursor's MCP documentation . Configuration file location : Project-specific: /.cursor/mcp.json Global: ~/.cursor/mcp.json For a local MCP server using the DataRobot Agentic Starter on port 9000: For a local MCP server using the DataRobot MCP template on port 8080: For a deployed MCP server: Claude Desktop can connect to remote MCP servers over HTTP. Configure the MCP server in claude_desktop_config.json . Claude Desktop MCP docs For Claude Desktop's current MCP options, see Claude Desktop's MCP documentation . Configuration file location : macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\\Claude\\claude_desktop_config.json For a local MCP server using the DataRobot Agentic Starter on port 9000: For a local MCP server using the DataRobot MCP template on port 8080: For a deployed MCP server: If your deployment requires authentication, add the appropriate headers or token mechanism as supported by Claude Desktop and your MCP server (e.g., environment variables or config fields; check Claude Desktop documentation and your server docs). Debugging Claude Desktop MCP On macOS, MCP-related logs are often under ~/Library/Logs/Claude/ (e.g., mcp*.log ). Use them to troubleshoot connection or auth issues. VS Code (with GitHub Copilot) includes built-in MCP client support. Configure remote MCP servers in an mcp.json file (HTTP servers use type , url , and optional headers ). VS Code Copilot MCP docs For VS Code's current MCP options, the configuration schema, and security notes, see Use MCP servers in VS Code and the MCP configuration reference . Configuration file location : Workspace: /.vscode/mcp.json (appropriate for shared, non-secret settings that you may commit to source control). User profile: run MCP: Open User Configuration from your coding environment's Command Palette ( \u21e7\u2318P / Ctrl+Shift+P ). Prefer this location, or another secret-management mechanism, for API keys and other credentials. If your MCP configuration includes headers such as Authorization or x-datarobot-api-token , do not commit those secrets to source control. Keep credentialed configuration in your user-profile mcp.json or another secure secret store. You can also use MCP: Add Server in your coding environment's Command Palette for a guided setup. For more options, see Add and manage MCP servers in VS Code . For a local MCP server using the DataRobot Agentic Starter on port 9000: For a local MCP server using the DataRobot MCP template on port 8080: For a deployed MCP server: If your deployed server requires a token, configure authentication using HTTP headers supported by your MCP server. Avoid passing tokens in URLs or query parameters to reduce the risk of credential leakage; see Use MCP servers in VS Code and your server docs. Apply and verify \u00b6 To confirm connectivity: Save your MCP configuration in the correct location. Restart your MCP client (or reload the window). In your MCP client, ask the AI to list or use tools from the DataRobot MCP server. Troubleshooting \u00b6 Client cannot connect to MCP server \u00b6 Symptoms : The IDE or Claude reports that the MCP server is unavailable, or tools do not appear. Solutions : Confirm the MCP server is running: Local: run curl -i http://localhost:9000/ or curl -i http://localhost:8080/ (adjust the port to match your local MCP server setup). A successful response indicates the server is up. Deployed: run curl -i using the exact URL from your deployment output. If the server requires authentication, you may need to pass a bearer token or other headers; see your deployment documentation. A successful response indicates the server is up. Check URL and path: Use the exact base URL and path (e.g., /mcp ) required by your client and server. Confirm firewall and network access: For deployed servers, ensure your network allows outbound HTTPS to the DataRobot host. Tools do not appear in the client \u00b6 Symptoms : Connection seems OK, but the client does not list MCP tools. Solutions : Restart the client (Cursor, Claude Desktop, or VS Code) after changing the MCP config. Check client and MCP server logs for errors (e.g., Cursor: Output \u2192 MCP Logs; Claude: ~/Library/Logs/Claude/ ). A successful connection will show MCP server logs with tool registration and availability information. Authentication errors \u00b6 Symptoms : Requests to the deployed MCP server return 401 or similar. Solutions : Confirm the deployed server's auth requirements (e.g., bearer token). Ensure the client is configured with the correct token or credentials (environment variables or extension settings). For DataRobot deployments, ensure DATAROBOT_API_TOKEN (or the equivalent used by the server) is valid and has access to the deployment. Additional resources \u00b6 Integrate tools using an MCP server \u2014 Use an MCP server from a DataRobot agentic workflow (e.g., LangGraph agent). DataRobot MCP template \u2014 Build and deploy standalone MCP servers with DataRobot integration. DataRobot MCP template \u2014 Configure your MCP client \u2014 Client setup details from the template repo. DataRobot Agentic Starter template \u2014 Full agentic application including an MCP server. Model Context Protocol \u2014 Official MCP specification.", "result_1_title": "Connect agentic coding environments to MCP servers: DataRobot docs", "result_1_url": "https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-mcp-clients.html", "status": "success", "total_results": 2 } }, "tool_result_checked": true, "tool_schema_bytes": 1668, "tools": [ "search_datarobot_agentic_docs", "datarobot_docs_fetch_page" ], "transport": "streamable-http", "url": "http://127.0.0.1:8080/mcp", "working": true }, "evidence_strength": "readme_checked", "example_checks": [ { "claim": "Standalone quickstart walks through LLM Gateway, drtools and running an agent", "command": "", "deferred": false, "evidence": "The quickstart is e2e-tests/examples/quickstart.ipynb and requires a DataRobot account (DATAROBOT_API_TOKEN, DATAROBOT_ENDPOINT) for LLM Gateway and deployments; no credentials are available in this guest. Ran the offline drtools MCP docs-search example instead via probe.json.", "expected": "documented command completes successfully", "input": "", "kind": "example", "outcome": "The quickstart is e2e-tests/examples/quickstart.ipynb and requires a DataRobot account (DATAROBOT_API_TOKEN, DATAROBOT_ENDPOINT) for LLM Gateway and deployments; no credentials are available in this guest. Ran the offline drtools MCP docs-search example instead via probe.json.", "source": "README.md#Standalone end-to-end examples", "verdict": "blocked" } ], "status": "PASS" }, "deviations": [ "README gives no server launch command; used the Taskfile drmcp-dev entrypoint with the venv python instead of uv run.", "Port is 8080 (FASTMCP_PORT env was ignored); HTTP transport is the default for server.py.", "Without DataRobot credentials the server registers 0 tools; enabling ENABLE_DR_DOCS_TOOLS=true was needed to get a callable tool. The docs search tool calls the public docs site over the network.", "Installed from the local workspace source rather than PyPI so the pinned commit is tested; removed build/ and egg-info outputs afterwards.", "Quickstart notebook example not runnable (needs DataRobot account); recorded as testable:false.", "Cycle check: no product scheduler/cron/webhook found, detected=false.", "One harmless 'kill: Operation not permitted' message appeared during cleanup of an unrelated PID; no effect on the run." ], "fault_owner": null, "main_blocker": null, "operation": "Start the datarobot-genai DataRobot MCP server (drmcp) from the README-installed package and call a real drtools tool over streamable HTTP: search_datarobot_agentic_docs returned live docs results including 'MCP: DataRobot docs'.", "operation_scope": "Offline scope only: Python 3.12 venv (README requires 3.11-3.13), pip install of the local pinned source with the [drmcp] extra, server started via the repo's documented dev entrypoint src/datarobot_genai/drmcp/server.py with ENABLE_DR_DOCS_TOOLS=true. DataRobot-account features (LLM gateway, deployments, dynamic tool registration, quickstart notebook) were NOT exercised because no DATAROBOT_API_TOKEN is available;", "outcome": "working", "plain_failure": null, "steps": [ "python3 -m venv && pip install --upgrade pip", "pip install \"\" (README Installation extra drmcp, from pinned source)", "Read Taskfile .taskfiles/drmcp.yml drmcp-dev: uv run src/datarobot_genai/drmcp/server.py (run with venv python instead of uv)", "ENABLE_DR_DOCS_TOOLS=true python src/datarobot_genai/drmcp/server.py (server listens on 127.0.0.1:8080/mcp)", "MCP initialize, notifications/initialized, tools/list -> search_datarobot_agentic_docs, datarobot_docs_fetch_page", "tools/call search_datarobot_agentic_docs {query:'MCP server setup',max_results:2} -> status success, title 'MCP: DataRobot docs'", "python3 -I --validate --replay -> valid true, replay PASS" ], "stuck_step": null, "suggested_fix": "For full coverage, provide a DATAROBOT_API_TOKEN and DATAROBOT_ENDPOINT in the guest to exercise DataRobot-backed MCP tools, dynamic tool registration and the quickstart notebook. Consider documenting the drmcp server launch command and the ENABLE_* tool flags in the README." }