Moltline Code Review & AI-Code Verifier

Ai Code Smell Scan

ai_code_smell_scan

Read-onlyIdempotent

Flag the tells of unreviewed AI-generated code in a source file. FREE.

Detects comments that restate the next line, leaked assistant preambles, placeholder TODOs, shipped 'Example usage' blocks, over-broad try/except that swallows errors, and auto-named identifiers. Typical input {"code": ""} returns {"reviewed_confidence": 0-100, "hits": [{"smell": "...", "evidence": ""}], "reading": "...", "note": "..."}.

Use on a full source file suspected of unreviewed machine authorship. Not on a diff (review_diff), and the result is a signal to check, not proof of authorship. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`code`	Yes	Full source text to scan, any language; paste the file contents as a single string.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds critical behavioral details: error handling ('never raises a protocol error — it returns an error object with fix instructions') and idempotency guarantee ('after correcting the input it is always safe to retry'). No contradictions with annotations.

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 efficiently structured: purpose first, then examples, usage guidelines, error behavior, and idempotency note. Every sentence adds value. Minor extraneous text ('FREE') but overall excellent conciseness.

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?

Covers almost all aspects: what smells are detected, input/output format, error handling, usage context, and intentional limitations (signal vs. proof). The presence of an output schema further reduces the burden on the description. Very comprehensive.

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% with a clear parameter description. The description adds only a typical input example, which is helpful but does not significantly extend the schema's meaning. Baseline 3 is appropriate.

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 begins with a specific verb and resource: 'Flag the tells of unreviewed AI-generated code in a source file.' It clearly identifies the tool's unique role among siblings (vs. review_diff) by focusing on full source files and AI smeels.

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 states when to use ('on a full source file suspected of unreviewed machine authorship'), when not to use ('Not on a diff'), and names the alternative tool ('review_diff'). It also clarifies the result is a 'signal to check, not proof of authorship,' setting proper expectations.

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

All Moltline Code Review & AI-Code Verifier tools Inspect

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TDQS

A4.6/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: AI smell detection, structural complexity, persona loading, checklist generation, diff risk scanning, secret scanning, and security deep dive. No two tools overlap in function, and descriptions explicitly state what each should not be used for.

Naming Consistency4/5

All tool names use snake_case and are readable. However, the pattern varies: some are verb_noun (get_reviewer_persona, secret_scan) while others are noun phrases (complexity_report, review_diff). This minor inconsistency prevents a perfect score.

Tool Count5/5

Seven tools cover the code review domain well without being excessive. Each tool addresses a specific aspect of code quality, security, and AI authorship detection. The scope is focused and no tool seems superfluous.

Completeness4/5

The tool set covers most analysis needs for code review: full-file scans, diff analysis, secret detection, security audit, and context like persona and checklist. A minor gap is the lack of an aggregation tool to combine results into a unified review report, but the core analysis workflows are complete.

Resources

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