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ralph_run_tools

Execute predefined development tool presets like test runners and linters for JavaScript, Python, Rust, and Go projects to automate code quality checks.

Instructions

Run external tool presets (test runners, linters, etc.).

Available presets:

  • javascript-test: Run npm test

  • javascript-lint: Run ESLint

  • python-test: Run pytest

  • python-lint: Run ruff

  • rust-test: Run cargo test

  • rust-lint: Run clippy

  • go-test: Run go test

  • build: Verify project builds

Use ralph_detect_tools to see which presets are available for your project.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
presetsYesList of tool preset names to run
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes what the tool does (run presets) and lists examples, but lacks details on execution behavior (e.g., sequential vs. parallel runs, error handling, output format, or side effects). It adds some context but not comprehensive behavioral disclosure.

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?

The description is well-structured and front-loaded, starting with the core purpose, followed by a bulleted list of presets, and ending with a usage tip. Every sentence adds value without redundancy, making it efficient and easy to scan.

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

Completeness4/5

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

Given the tool's moderate complexity (executing external tools), no annotations, and no output schema, the description does well by listing presets and referencing detection. However, it could improve by mentioning execution details (e.g., output handling or errors). It's mostly complete but has minor gaps in behavioral context.

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

Parameters4/5

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 the 'presets' parameter. The description adds value by listing specific preset names (e.g., javascript-test, python-lint) and clarifying their purposes, which goes beyond the schema's generic description. With 0 parameters beyond the schema, baseline is 4, and the description enhances understanding.

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 verb ('run') and resource ('external tool presets') with specific examples like test runners and linters. It distinguishes from siblings by focusing on execution rather than detection (ralph_detect_tools) or listing (ralph_list_tools).

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

Usage Guidelines5/5

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

It explicitly states when to use this tool (for running presets) and when to use an alternative (ralph_detect_tools to see available presets). It provides clear context by listing available presets and referencing sibling tools for complementary actions.

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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