$ loading_
Track AI interactions, analyze code structure, and monitor real-time token efficiency.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Corn Intelligence MCP Server" yet — see the docs or source repo.
Summarize AI interaction data from the last 7 days by model, including request volume, average latency, failure rate, and token usage, and highlight unusual spikes.
An AI interaction analytics report grouped by model with key metrics and anomaly notes.
Perform AST analysis on this Python code, identify functions, classes, imports, and complex logic nodes, and summarize potential refactoring opportunities.
A structured code analysis showing major syntax nodes and optimization suggestions.
Review token efficiency in the current AI workflow, identify high-consumption steps, and suggest ways to reduce cost and improve response efficiency.
A token efficiency diagnostic report with bottlenecks and optimization recommendations.
Helps AI coding agents analyze code, retain semantic memory, and enforce quality.
Analyze MCP tool security risks, detect malicious behavior, and provide risk scores.
Analyze codebases with semantic search, dependency insights, and natural language Q&A.
Track AI usage, costs, logs, and debug model interactions across apps.
Analyze code structure and Git history while drastically reducing AI token usage.
Build, debug, and manage software tasks with natural language across LLMs.