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Moltline Code Review

by com.moltlinestudio

Get Reviewer Persona

get_reviewer_persona
Read-onlyIdempotent

Load the Senior Reviewer persona for consistent, high-signal reviews. PREMIUM (license).

The persona is a reviewing voice that is skeptical, specific, and kind — demands evidence over vibes and blocks only on real risk. Takes no arguments. Returns {"persona": ..., "identity": ..., "rules": ["...", ...], "opening_move": "..."} ready to adopt as a system prompt.

Use to keep repeated reviews consistent in voice and rigor. Not for running a review - the scan tools do that. 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
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.8/5.0
Behavior5/5

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

Goes well beyond annotations: specifies error handling (never raises protocol error, returns error object), return format, idempotency, and read-only nature. No contradiction 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.

Conciseness4/5

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

Well-structured and front-loaded. The persona description (skeptical, specific, kind) is slightly verbose but adds context. Could be trimmed without losing clarity, but still effective.

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 0 parameters, existing output schema, and sibling list, the description covers purpose, usage, behavioral traits, return format, error handling, and differentiation. Nothing significant is missing.

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?

With 0 parameters and 100% schema coverage, baseline is 4. The description adds value by stating 'Takes no arguments' and explaining error behavior for invalid input, which is relevant beyond the empty schema.

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 loads the 'Senior Reviewer persona' for consistent reviews. It distinguishes itself from sibling tools by explicitly saying 'Not for running a review - the scan tools do that.'

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?

Provides explicit when-to-use ('keep repeated reviews consistent'), when-not-to-use ('not for running a review'), and alternatives ('scan tools'). Also mentions retry safety and license (PREMIUM).

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 toolsInspect
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TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a distinct analysis area: AI-generated code smells, structural complexity, reviewer persona, review checklist, diff risk scanning, secret scanning, and OWASP security deep dive. Even though review_diff and security_deep_dive both touch security, one is diff-based and the other is full-file, and descriptions clarify the difference.

Naming Consistency4/5

All names use snake_case, but there is a mix of verb-first (ai_code_smell_scan, get_reviewer_persona, secret_scan) and noun-first (complexity_report, review_checklist, review_diff, security_deep_dive) patterns. This is mostly consistent but the verb usage varies (scan, get, report, checklist, dive).

Tool Count5/5

7 tools is well-scoped for a code review analysis server. Each tool serves a specific purpose without overlap, covering multiple angles (AI smells, complexity, security, secrets, diff review, checklist, persona) without being overwhelming.

Completeness4/5

The tool set covers essential static analysis tasks for code review: structural, security, secret detection, and AI-generated code detection. It also provides supporting tools (checklist, persona). A minor gap is the lack of an integrated tool that produces a consolidated review summary or comment generation from findings.

Resources