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chatgpt_review

Requests an adversarial review from ChatGPT on code, diffs, or snippets, targeting security, performance, or architecture.

Instructions

Requests an adversarial code review from ChatGPT on a git diff, file content, or code snippet with specialized focus.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
focusNoReview focus area: security, performance, architecture, or thorough. Default is thorough.thorough
modelNoModel to use for review (default: o3-mini).o3-mini
diff_or_codeYesThe code snippet or git diff to review.
instructionsNoSpecific review instructions or questions.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.4.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must disclose all behavioral aspects. It states the tool performs an 'adversarial code review' but doesn't mention whether it's read-only, any side effects, authentication requirements, or rate limits. It also doesn't describe the return format or potential side effects on external systems.

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 a single sentence that is clear and to the point. It front-loads the action and resource, though it could be slightly more specific about the 'specialized focus' without adding length.

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

Completeness2/5

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

Given there is no output schema and no annotations, the description fails to explain what the tool returns, how to interpret the review, or any prerequisites. The tool has four parameters including an enum, but the description doesn't elaborate on the review process, output format, or typical use cases. It is insufficient for an agent to understand the full context.

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%, so the schema already documents all four parameters. The description adds nothing beyond what the schema provides, such as clarifying the meaning of 'focus' or how 'instructions' interact with the review. Baseline 3 is appropriate since the schema carries the burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as requesting an adversarial code review from ChatGPT, specifying the input types (git diff, file content, or code snippet). It differentiates from the sibling chatgpt_consult, which is likely a general consultation tool, though it doesn't explicitly contrast them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like chatgpt_consult or the codex_* family. The mention of 'specialized focus' is vague and doesn't clarify the intended context or conditions for selection.

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