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Search the web, run code, and continue AI-assisted workflows seamlessly.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-ai-assistant-iris" yet — see the docs or source repo.
Search for recent resources on vector database performance optimization, summarize five key findings, and use Python to group them by theme into a table.
A sourced summary of findings plus a code-generated structured table.
I have sales data with duplicates and missing values. Write and run a Python cleaning script, explain each step, and output summary stats of the cleaned result.
A runnable cleaning script with explanations and a summary of cleaned results.
Using the previous response context, continue debugging this API call code: identify the error cause, provide a fixed version, and explain the changes.
A context-aware follow-up analysis with corrected code and debugging notes.
Route and manage multi-model chats via MCP with fallback and session memory.
Search and navigate multiple code repositories with natural language understanding.
Enable AI agents to search the web and extract useful page content.
Enable AI agents to search the web and retrieve structured results.
Lets AI read latest sensor data from MQTT and Sparkplug B streams.
Chat with AI to retrieve documents and trigger MCP-powered tools.