Moltline Code Review & AI-Code Verifier

Review Diff

review_diff

Read-onlyIdempotent

Risk-scan a unified diff the way a senior reviewer triages a PR. FREE.

Flags added lines matching known risk patterns — injection sinks, disabled TLS, bare excepts, debug prints, TODOs, N+1 hints, leaked secrets — with the new-file line number and a severity (1 low - 4 high). Typical input {"diff": ""} returns {"added_lines": N, "risk_score": 0-100, "verdict": "...", "secrets": [...], "findings": [{"line": N, "severity": 1-4, "issue": "...", "code": "..."}], "note": "..."}.

Use on a unified diff, when only the change matters. Not for whole-file analysis (complexity_report, ai_code_smell_scan). 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
`diff`	Yes	A unified diff exactly as produced by `git diff` — text with @@ hunk headers and +/- line prefixes. Only added (+) lines are scanned.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, which align with the tool's description of being read-only and idempotent. The description adds value by specifying that the tool never raises protocol errors on invalid input, instead returning a structured error message with guidance. This goes beyond the annotation coverage to clarify error behavior.

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?

The description is compact with 6 sentences. The first two sentences define purpose and output format, front-loading key info. The use of a free statement and example JSON are efficient. Minor redundancy: 'FREE' standalone could be integrated. Overall, every sentence adds value.

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 output schema exists and is detailed in the description, return values are well-covered. The description addresses edge cases (invalid input), maps to sibling tools, and includes error handling. A slight gap: no mention of rate limits or size limits for the diff, but not critical for typical use.

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 coverage is 100% but the description adds meaning by clarifying the expected format ('exactly as produced by `git diff` — text with @@ hunk headers'), the scanning scope ('only added (+) lines are scanned'), and by providing an example input/output structure. This compensates for the schema's brief description of 'diff'.

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 explicitly states the tool 'Risk-scan a unified diff' and distinguishes it from siblings like 'complexity_report' and 'ai_code_smell_scan' by noting it works only on changes, not whole files. The verb 'risk-scan' combined with 'unified diff' clearly delimits the resource and action.

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?

The description provides explicit guidance on when to use ('on a unified diff, when only the change matters') and when not to ('not for whole-file analysis'), naming alternatives (complexity_report, ai_code_smell_scan). It also includes error recovery instructions by noting the tool returns an error message and is safe to retry after input correction.

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

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?