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validate_skill

Check SKILL.md files for correct structure, naming conventions, and completeness to ensure they're ready for testing in AI agent development workflows.

Instructions

Validate a SKILL.md file for correct structure, naming conventions, and completeness. Call this after writing or editing a SKILL.md before running tests. Returns a list of issues found and whether the skill is valid.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skill_pathYesPath to the SKILL.md file or directory containing skills (e.g. '.claude/skills/my-skill/SKILL.md')

Implementation Reference

  • The tool 'validate_skill' is implemented as a call to the 'evalview skill validate' CLI command within the MCP server's tool handler.
    elif name == "validate_skill":
        skill_path = os.path.normpath(args.get("skill_path", ""))
        if not skill_path:
            return "Error: 'skill_path' is required."
        cmd = ["evalview", "skill", "validate", skill_path]
Behavior4/5

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

With no annotations, the description carries full burden and does well: it discloses the tool's behavior (validation, not execution), output format ('list of issues found and whether the skill is valid'), and purpose (pre-test validation). It doesn't mention error handling or performance, but covers core behavior adequately.

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?

Two sentences, zero waste: first defines purpose and usage timing, second specifies output. Every phrase adds value, and it's front-loaded with the core action.

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?

For a single-parameter validation tool with no annotations and no output schema, the description is nearly complete: it explains what it does, when to use it, and what it returns. It could detail output structure (e.g., issue types) but covers essentials given the simplicity.

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 the single parameter 'skill_path'. The description adds no additional parameter semantics beyond implying it validates SKILL.md files, which is redundant with the schema's description. Baseline 3 is appropriate when schema does the work.

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 verb 'validate' and resource 'SKILL.md file' with explicit scope: 'for correct structure, naming conventions, and completeness.' It distinguishes from siblings like 'run_check' or 'run_skill_test' by focusing on validation rather than execution.

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?

Explicit guidance is provided: 'Call this after writing or editing a SKILL.md before running tests.' This tells the agent when to use it (post-editing, pre-testing) and implies alternatives like 'run_skill_test' for actual testing.

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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