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generate_ai_standards

Automatically generate AI assistant instruction files from existing project configuration files like .editorconfig, ESLint, and pyproject.toml for Claude, GitHub Copilot, and Cursor.

Instructions

Auto-generate AI assistant instruction files (CLAUDE.md, .github/copilot-instructions.md, .cursor/rules/standards.mdc) from existing project config files (.editorconfig, .prettierrc, ESLint, pyproject.toml, etc.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatsNoWhich instruction formats to generate (default: all)
project_pathNoPath to project root (default: current directory)
Behavior2/5

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

With no annotations provided, the description carries full burden but only states what the tool does, not behavioral traits. It doesn't disclose whether this is a read-only operation, what permissions are needed, whether files are overwritten, or what happens if config files are missing. For a file generation tool with zero annotation coverage, this is insufficient.

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

Conciseness5/5

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

Single sentence efficiently conveys the core functionality with specific examples of both input and output files. Every word earns its place with zero wasted text, making it easy to parse quickly.

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?

For a file generation tool with no annotations and no output schema, the description should do more. It doesn't explain what the generated files contain, how they're structured, whether they overwrite existing files, or what happens on errors. With 2 parameters and significant behavioral implications, this is incomplete.

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 description coverage is 100%, so the schema already documents both parameters well. The description mentions generating from 'existing project config files' which provides context for the project_path parameter, but adds minimal value beyond what the schema provides. Baseline 3 is appropriate when schema does the heavy lifting.

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?

The description clearly states the specific action ('Auto-generate') and target resources ('AI assistant instruction files') with explicit file name examples. It distinguishes from siblings by focusing on generation from config files rather than operations like adding, exporting, or updating episodes.

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?

The description implies usage when needing to create instruction files from existing configs, but doesn't explicitly state when to use this vs alternatives like 'suggest_claudemd_update' or 'update_claudemd'. No explicit exclusions or prerequisites are mentioned.

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

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