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Read benchmark data and related information for OPERANT AI agent calibration.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "operant-mcp" MCP server from askskill: Run: claude mcp add 'io-github-saagpatel-operant-mcp' -- npx -y saagar-operant-mcp
Read the available datasets, field descriptions, and read-only resource list in the OPERANT AI operating-agent calibration benchmark, and organize them hierarchically for me.
A structured overview of benchmark resources, field definitions, and data organization.
Read a representative set of agent calibration evaluation samples from the benchmark and summarize each sample's task goal, input format, and evaluation dimensions.
A summary of several evaluation samples to help understand the benchmark task design.
Based on the read-only contents of the OPERANT benchmark, prepare a research note explaining the calibration problems it targets, the available data types, and suitable directions for comparative analysis.
A research-oriented summary note covering the benchmark's purpose and analysis angles.
Provide AI agents with coding standards, testing, planning, and requirements guidance.
Simulate dynamic actions for any query with persistent state across sessions.
Read and manage OpenProject data with guarded tools and confirmed write actions.
Build, debug, and manage software tasks with natural language across LLMs.
Test all MCP protocol features when building and validating clients.
Connect to the Opiny API via MCP in local or remote environments.