Gemini Code Reviewer

by iamrichardD

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Latest release: v2.1.1
Disambiguation2/5
The gemini_code_review, gemini_analyze_code, and gemini_suggest_improvements tools overlap heavily, as code review typically includes analysis and improvement suggestions. While gemini_validate_architecture and gemini_propose_plan are distinct, the three overlapping tools create boundary ambiguity.
Naming Consistency2/5
Naming is inconsistent: get_review_history uses a get_ prefix while the rest use gemini_, and the verb/noun structure varies (gemini_code_review is noun-led, while others are verb-led like gemini_analyze_code). No uniform verb_noun pattern is applied.
Tool Count5/5
Six tools is a well-scoped set for a code review server, covering review, analysis, suggestions, architecture validation, and planning without excess.
Completeness4/5
The tool set covers the core lifecycle of code review (review, analyze, improve, validate architecture, plan). Minor gaps exist such as a dedicated security review or merge/reporting capability, but these do not critically undermine the domain.
Average 3.1/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It does not state whether the tool is read-only, whether it has side effects, or what output it produces. The analysis_type enum includes potentially misleading values like 'refactor' and 'optimize' without explaining whether code is actually modified.
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 concise sentence that communicates the core purpose without unnecessary words. However, the brevity contributes to the under-specification of usage details, making it less useful than it could be.
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 the lack of annotations, output schema, and multiple analysis modes, a single sentence is insufficient. The description does not explain the behavior of different analysis_type values, expected outputs, or prerequisites, making the tool difficult to use correctly.
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?
The description adds no information about the parameters beyond the schema. Schema coverage is only 67%, and the analysis_type parameter lacks a description, yet the description does not compensate by explaining the enum values or how file_path and language are used.
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 states the tool's function: using Gemini CLI to analyze and explain code functionality. It uses specific verbs ('analyze', 'explain') and a resource ('code'), but does not differentiate from sibling tools like gemini_code_review or gemini_suggest_improvements, leaving room for ambiguity.
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?
There is no guidance on when to use this tool versus sibling tools, nor any mention of alternatives or exclusions. The description is too generic to help an agent decide between this and other Gemini-based analysis tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only mentions 'Use Gemini CLI' without revealing whether the tool modifies the file, requires authentication, or what the output format is. This is a critical omission.
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 a single, front-loaded sentence with no redundancy. It is concise and direct.
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?
With no output schema and no annotations, the description is incomplete. It does not explain what the agent should expect after invocation (e.g., suggestions rendered inline, a diff, or a report). The minimal text leaves too much unspecified for a tool with moderate complexity.
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?
The schema description coverage is 67%, with descriptions for file_path and language. The description adds no parameter semantics beyond the schema. The improvement_goals enum is self-explanatory but not explained in the description, so baseline 3 is appropriate.
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 states 'Use Gemini CLI to suggest specific improvements for code', which clearly identifies the action (suggest) and resource (improvements for code). It is distinct from sibling tools like gemini_code_review or gemini_analyze_code, though it doesn't explicitly differentiate 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?
There is no guidance on when to use this tool versus alternatives. The description neither mentions appropriate use cases nor exclusions. Usage context is only implied by the tool name and improvement_goals parameter.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits. It only states the purpose, not whether the tool modifies files, requires permissions, produces reports, or any side effects. 'Validate' implies non-mutating analysis, but details are absent.
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 concise sentence that is easy to parse and front-loads the primary action. It could be expanded with usage guidance, but as a minimal statement it is not verbose or redundant.
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?
The tool has multiple parameters, no output schema, and no annotations, yet the description only covers the basic purpose. It omits default behavior, expected output, and how validation_focus affects results, making it inadequate for an agent to fully understand the tool's scope.
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?
The schema provides descriptions for two of three parameters, but the description adds no parameter-level information. The validation_focus enum is self-explanatory but lacks a description; the tool description does not clarify defaults or how parameters interact.
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 states the tool validates code architecture and design patterns, with a specific verb and resource. It does not explicitly distinguish itself from sibling tools like gemini_code_review or gemini_analyze_code, so it lacks sibling differentiation.
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?
No guidance is given on when to use this tool versus the sibling tools. The description only says 'Use Gemini CLI to validate code architecture and design patterns' without explaining appropriate contexts or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the full burden of behavioral disclosure. It does not state whether the tool reads only, modifies files, has side effects, or what the return value looks like. This is a significant gap for an external CLI invocation.
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 a single, focused sentence that immediately states the tool's purpose. It is appropriately concise with no wasted words.
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?
The tool has no output schema and no annotations, and the description does not cover return format, side effects, external dependencies (Gemini CLI), or how it fits among siblings. This leaves the agent with insufficient context for safe and effective invocation.
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?
The schema provides descriptions for 75% of the parameters, so the baseline is near 3. The description mentions aspects like 'correctness' and 'improvements' which loosely align with focus_areas, but it does not add direct parameter-level meaning beyond the schema.
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 states the tool's action ('review code') and scope ('for correctness, best practices, and improvements'). It does not explicitly distinguish from sibling tools like gemini_analyze_code or gemini_suggest_improvements, but the purpose is specific enough to be understood.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of common use cases, prerequisites, or exclusions, leaving the agent without clear decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the operation is a retrieval ('Get') and scopes results to 'this session', implying no side effects. However, it does not describe the return format, ordering, or what constitutes an 'operation', which limits transparency.
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 a single, concise sentence with a clear front-loaded verb and object. Every word contributes to meaning, and there is no redundant or filler content.
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?
Given the tool's simplicity (zero parameters, no annotations, no output schema), the description is minimally adequate but leaves gaps. It specifies the purpose and session scope, but does not explain what the returned history includes, its format, or how operations are ordered, which is ambiguous for a retrieval tool without an output schema.
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?
The tool accepts zero parameters, so the baseline is 4. The description's reference to 'this session' adds meaningful context about the implicit scope, which is helpful for understanding how the tool behaves without parameters.
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 uses a specific verb 'Get' and identifies the resource as 'the history of operations performed in this session', which clearly distinguishes it from sibling tools that review, analyze, or suggest. The scope is explicit and unambiguous.
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. It simply states what it does, leaving the user to infer usage context from the session-scope wording. There are no exclusions or references to related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the burden. It discloses that the tool generates plans and that they are intended for execution by another AI, implying no direct execution. However, it does not explicitly state side effects, permissions, or limits (e.g., whether it calls a Gemini service or is purely local). This is a moderate level of transparency.
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 two sentences long, front-loaded with the primary action, and contains no redundant information. Every phrase adds value: 'Use Gemini CLI,' 'generate a detailed implementation plan,' and 'for another AI to follow.'
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?
For a tool with two parameters and no output schema, the description adequately conveys the purpose and the nature of the generated plans ('structured, step-by-step'). It does not specify the exact output format or error scenarios, but given the tool's simplicity, this is a minor gap.
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?
The input schema already provides full descriptions for both parameters (prompt and conversation_history), achieving 100% coverage. The tool description adds no additional semantic information about the parameters, so it does not exceed the baseline.
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's function: 'generate a detailed implementation plan for another AI to follow.' This is a specific verb+resource combination that distinguishes it from sibling tools like code review or architecture validation, which focus on analysis rather than planning.
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 implies when to use the tool (when another AI needs a plan to execute) but does not explicitly mention alternatives or exclusions. The context is clear enough for an agent to infer the primary use case, though it lacks a direct contrast with sibling tools.
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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