$ loading_
Run SQL, APIs, and sandboxed Python for multi-step research and data tasks.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCP Agent Toolkit" yet — see the docs or source repo.
Connect to the read-only database and query sales by region for the last 30 days. Then call the internal REST API to fetch campaign data. Use Python to calculate sales changes before and after each campaign, and output a table with conclusions.
SQL results, a merged analysis table, and a brief conclusion on campaign effectiveness.
Call the specified REST API to fetch user behavior data, check for missing values, duplicate records, and abnormal fields. Use Python to produce a summary of the cleaned CSV and list the data quality issues found.
A data quality report, cleaning summary, and a description of the structured data ready for further processing.
First use an API to fetch public metrics for an industry, then query internal historical data with read-only SQL, and use Python for year-over-year and trend analysis. Finally, compile a research summary with key findings and next-step recommendations.
A concise report with external vs. internal data comparisons, trend analysis, and research recommendations.
Connect AI agents to 1C:Enterprise data through MCP and REST APIs.
Manage MySQL databases with natural language for queries, CRUD, and monitoring.
Equip AI agents with Chinese-first search, file handling, and shell execution.
Create, manage, and compose AI agents for MCP-compatible clients and tools.
Query and manage Microsoft SQL Server databases with natural language.
Use natural language to load data, train models, and track experiments.