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SKILL.md Lint

by com.moltlinestudio

Json Validate

json_validate
Read-onlyIdempotent

Validate JSON text and summarize its structure. FREE.

Typical input {"text": "{"a": [1, 2]}"} returns {"valid": true, "structure": {"a": ["array[2]", "int"]}}; invalid JSON returns {"valid": false, "error": "", "line": N, "column": N} instead.

Use when JSON may be malformed or its shape is unknown. Reports structure, not conformance to a schema. Not for testing a pattern (regex_test). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "text is empty — pass the JSON document as a raw string"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe JSON document to validate, as a raw string; must be non-empty.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint, making basic behavioral profile clear. The description adds valuable detail: explains error handling behavior (never raises protocol error, returns structured error), idempotency ('read-only and idempotent'), and the constraint that it reports structure not conformance. Loses a point because it doesn't disclose the return format beyond the example (e.g., the structure format for deeply nested JSON).

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

Conciseness4/5

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

Well-structured with a clear opening sentence, example usage, usage guidance, and error handling details. Slightly verbose with the error example inline, but overall efficient. Could condense the error paragraph slightly.

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

Completeness5/5

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

Given the tool is simple (1 param, read-only, rich annotations), the description is complete enough. It covers purpose, usage, input format, output format, error handling, and safety guarantees. No gaps remain for an agent to select and invoke it correctly.

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 coverage is 100%, with a good description for the single 'text' parameter. The description adds minimal extra meaning beyond the schema — it provides an example of valid input format but does not elaborate on edge cases like whitespace or encoding. Baseline 3 is appropriate as 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 tool's purpose: 'Validate JSON text and summarize its structure.' It specifies the resource (JSON text), action (validate and summarize), and differentiates it from siblings like regex_test by mentioning 'Not for testing a pattern.'

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?

Explicitly advises when to use: 'Use when JSON may be malformed or its shape is unknown.' Provides a clear when-not-to-use by saying 'Not for testing a pattern (regex_test).' Also tells the agent what to do on errors: 'after correcting the input it is always safe to retry.'

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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TDQS

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: cron explanation, text diffing, JSON validation, skill linting, packaging layout validation, and regex testing. There is no functional overlap, so an agent can easily select the correct tool.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., cron_explain, diff_texts, lint_skill). The naming convention is uniform and predictable across the entire set.

Tool Count5/5

With only 6 tools, the server is well-scoped for its linting and validation purpose. Each tool is justified and contributes to the overall functionality without redundancy or excessive bloat.

Completeness4/5

The tool set covers the primary validation needs for skills (linting, packaging, and auxiliary utilities like cron, diff, JSON, regex). Minor gaps could include a tool for validating skill dependencies or conformance to a specific schema, but the core workflows are well-supported.

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