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execute_claude_script

Automatically detect and execute Python scripts from Claude LLM for bioinformatics analysis, with auto-installation of dependencies and generation of HTML reports including execution logs.

Instructions

🚀 MAIN TOOL: Automatically detect and execute Python scripts generated by Claude LLM for bioinformatics tasks. Features: ✅ Auto-detects Python installation ✅ Provides detailed installation guide if Python missing ✅ Auto-installs required packages (pandas, numpy, biopython, etc.) ✅ Full execution logging and error handling ✅ Script length monitoring (recommends ≤100 lines) ✅ HTML report generation with auto-browser opening

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claude_responseYesThe full response from Claude LLM that may contain Python scripts
workflow_idNoOptional workflow ID to associate with this execution
execution_contextNoContext about what the script is supposed to do
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description discloses several behavioral aspects: auto-detection, installation, package management, logging, script length monitoring, and report generation. However, it does not address potential destructive effects of script execution.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description uses bullet points and emojis which are clear but somewhat verbose. Could be more succinct.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool lacks an output schema, and the description does not explain what the tool returns to the agent (e.g., execution result, path to report). Missing this information for agent decision-making.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already provides descriptions for all three parameters. The tool description does not add further parameter semantics beyond what is in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool detects and executes Python scripts for bioinformatics tasks, distinguishing it from sibling tools that analyze tasks or debug workflows.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Despite listing features, the description does not provide explicit guidance on when to use this tool versus siblings. Usage context is implied but not stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

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