$ 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.
Securely equips AI agents with executable tools for commands, search, and file operations.
Give AI coding agents filesystem, Git, database, and compute tools via MCP.
Manage MySQL databases with natural language for queries, CRUD, and monitoring.
Securely run agent tools with isolation, permissions, and centralized MCP registry.
Query e-commerce SQLite data safely and answer business analytics questions.
Create, manage, and compose AI agents for MCP-compatible clients and tools.