rush

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rush_review

Review a code path for size, TODO density, docstrings, naming, and complexity issues. Returns status, findings, and summary, with optional LLM-based analysis.

Instructions

Review code at for size, TODO density, missing docstrings, naming, complexity. Returns {status, findings[], summary}. Default: heuristic. Pass use_llm=true to call configured model.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`path`	Yes
`use_llm`	No
`use_graft`	No
`changed_files`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`raw`	No
`tool`	No
`engine`	No
`status`	No
`metrics`	No
`summary`	No
`findings`	No
`metadata`	No
`artifacts`	No
`duration_ms`	No
`review_kind`	No
`engine_version`	No
`review_provider`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.2.2Sep 15, 2026

changedOutput schema / properties / metrics / anyOf

Previous value: -[
-  {
-    "additionalProperties": {
-      "anyOf": [
-        {
-          "type": "integer"
-        },
-        {
-          "type": "number"
-        },
-        {
-          "type": "string"
-        }
-      ]
-    },
-    "type": "object"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "additionalProperties": {
+      "anyOf": [
+        {
+          "type": "integer"
+        },
+        {
+          "type": "number"
+        },
+        {
+          "type": "string"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "type": "object"
+  },
+  {
+    "type": "null"
+  }
+]

First observedv0.3.0Aug 30, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations are provided, so the description must carry the burden. It discloses the return format ({status, findings[], summary}) and the two modes (heuristic vs LLM). However, it does not mention potential side effects like network calls or costs when using the LLM, nor does it explicitly state whether the operation is read-only, which is implied by 'review' but not confirmed.

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?

Two sentences with zero wasted words. The core purpose is front-loaded, and the mode switch is mentioned immediately after. Perfectly concise.

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

Completeness3/5

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

An output schema exists (though not provided in the prompt), so the return structure is covered. The description mentions the return format. However, the unexplained parameters (use_graft, changed_files) and lack of behavioral details about LLM side effects make it incomplete for an agent to use confidently without additional information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains use_llm (calling the configured model) but does not explain use_graft or changed_files at all. The path parameter is obvious from the description but not elaborated. With four parameters, only one is partially described, leaving significant gaps.

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 verb (review), the resource (code at <path>), and the specific criteria (size, TODO density, missing docstrings, naming, complexity). It is distinct from siblings like rush_lint or rush_complexity by being a holistic code review rather than a focused check.

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

Usage Guidelines4/5

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

The description provides clear guidance on the default heuristic mode and explicitly states when to use the LLM (pass use_llm=true). It does not explicitly mention when to avoid this tool in favor of alternatives, but it gives enough context for basic selection.

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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