Smart Code Reviewer

by renjismzy

Overview Schema Related Servers Score Discussions

TypeScript

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: code quality analysis, complexity calculation, security issue detection, documentation generation, and refactoring suggestions. An agent can easily differentiate between them based on their specific focus areas.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., analyze_code_quality, calculate_complexity) using snake_case throughout. The naming is predictable and readable across all five tools.
Tool Count5/5
With 5 tools, this server is well-scoped for a code review domain. Each tool earns its place by covering distinct aspects of code review (quality, complexity, security, documentation, refactoring), avoiding bloat or thinness.
Completeness5/5
The tool set provides complete coverage for a code review server, addressing key areas like quality analysis, complexity metrics, security scanning, documentation, and refactoring. There are no obvious gaps that would hinder an agent's workflow.
Average 2.9/5 across 5 of 5 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 burden for behavioral disclosure. It mentions detecting problems and suggesting improvements, but lacks details on what types of problems (e.g., bugs, performance, style), the format of suggestions, whether analysis is static/dynamic, computational requirements, or error handling. For a tool with no annotation coverage, this is insufficient.
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, efficient sentence in Chinese that directly states the tool's function. It's appropriately concise without unnecessary words, though it could be slightly more structured by separating problem detection from suggestion generation for clarity.
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 tool's complexity (code analysis with multiple potential outputs), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the analysis returns (e.g., issues list, scores, recommendations), how results are structured, or any limitations. This leaves significant gaps for an agent to use the tool effectively.
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 description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema (e.g., no examples, constraints, or usage notes). Baseline 3 is appropriate when the schema does the heavy lifting.
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 purpose: '分析代码质量，检测潜在问题和改进建议' (analyze code quality, detect potential problems and improvement suggestions). It specifies the verb 'analyze' and resource 'code quality' with additional outcomes. However, it doesn't explicitly differentiate from siblings like 'detect_security_issues' or 'suggest_refactoring' which might overlap in scope.
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. With siblings like 'detect_security_issues' and 'suggest_refactoring' that might handle similar aspects of code analysis, there's no indication of scope boundaries, prerequisites, or comparative use cases. This leaves the agent guessing about tool selection.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on behavioral traits: it doesn't specify if this is a read-only analysis, what the output format might be (since no output schema exists), potential performance implications, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its operation.
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 extremely concise—a single sentence in Chinese that directly states the tool's purpose without any fluff. It's front-loaded with the core action and resource, making it efficient and easy to parse. Every word earns its place by specifying the metrics involved.
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 complexity of calculating code complexity metrics, the description is incomplete. There are no annotations to clarify behavior, no output schema to explain return values, and the description doesn't address what the tool outputs or how it handles different languages or code structures. For a tool with 2 parameters and no structured support, more context is needed to guide effective use.
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 has 100% description coverage, with clear documentation for both parameters ('code' and 'language'). The description doesn't add any semantic details beyond what the schema provides (e.g., it doesn't explain what '圈复杂度' or '认知复杂度' entail in terms of input requirements). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate or enhance parameter understanding.
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 purpose: '计算代码复杂度指标（圈复杂度、认知复杂度等）' (calculate code complexity metrics like cyclomatic complexity, cognitive complexity, etc.). It specifies the verb '计算' (calculate) and the resource '代码复杂度指标' (code complexity metrics). However, it doesn't explicitly differentiate from sibling tools like 'analyze_code_quality' or 'suggest_refactoring', which might also involve complexity analysis.
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 doesn't mention sibling tools or contexts where this tool is preferred over others (e.g., 'analyze_code_quality' might include complexity as part of broader analysis). There's no indication of prerequisites, exclusions, or specific scenarios for usage.
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 provided, the description carries the full burden of behavioral disclosure. It states what the tool does (detects security issues) but doesn't describe how it behaves: whether it's read-only or destructive, what permissions are needed, how results are returned, or any rate limits. For a security analysis tool with zero annotation coverage, this leaves significant gaps in understanding operational characteristics.
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, efficient sentence in Chinese that directly states the tool's purpose with zero wasted words. It's appropriately sized and front-loaded with the essential information.
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 this is a security analysis tool with no annotations and no output schema, the description is incomplete. It doesn't explain what types of vulnerabilities are detected, how results are formatted, whether it's a lightweight scan or deep analysis, or any limitations. For a tool that presumably returns important security findings, more context about output and behavior is needed.
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 description coverage is 100%, so the schema already documents both parameters (code and language) with descriptions. The tool description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
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 '检测代码中的安全漏洞和风险' clearly states the tool's purpose as detecting security vulnerabilities and risks in code, using specific verbs ('检测') and resources ('代码中的安全漏洞和风险'). It distinguishes from siblings like analyze_code_quality (general quality) and calculate_complexity (complexity metrics), but doesn't explicitly mention this differentiation in the description text itself.
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 analyze_code_quality or suggest_refactoring. It doesn't specify prerequisites, context, or exclusions. The agent must infer usage from the purpose alone without explicit direction.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('生成' - generate) but doesn't disclose any behavioral traits like whether this is a read-only operation, if it requires specific permissions, what the output format might be, or any rate limits. For a tool with no annotation coverage, this is a significant gap in 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 extremely concise and front-loaded: '为代码生成详细的文档和注释' (generate detailed documentation and comments for code). It's a single, clear sentence with no wasted words, making it easy to understand at a glance. Every word earns its place by directly stating the tool's purpose.
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 complexity of a documentation generation tool with 3 required parameters and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., formatted documentation text), any behavioral aspects like error handling, or how it interacts with sibling tools. With no annotations and no output schema, the description should provide more context to be fully helpful.
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 description adds no meaning beyond what the input schema provides. The schema has 100% description coverage, with clear parameter descriptions (e.g., '要生成文档的代码' - code to generate documentation for, '编程语言' - programming language, '文档类型' - documentation type with enum values). Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate with additional semantic context.
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 purpose: '为代码生成详细的文档和注释' (generate detailed documentation and comments for code). It specifies the verb '生成' (generate) and the resource '文档和注释' (documentation and comments). However, it doesn't explicitly differentiate from sibling tools like 'analyze_code_quality' or 'suggest_refactoring', which might also involve code analysis but for different purposes.
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 doesn't mention any context, prerequisites, or exclusions, such as when to choose 'generate_documentation' over 'suggest_refactoring' for code improvement tasks. Without such guidance, users might struggle to select the appropriate tool among siblings.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool provides suggestions to improve code structure and readability, implying a read-only, advisory function. However, it lacks details on critical behaviors: whether it modifies code, requires specific permissions, has rate limits, returns structured advice or examples, or handles errors. For a tool with no annotations, this is a significant gap in 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 extremely concise and front-loaded: a single sentence in Chinese that directly states the tool's purpose. There's no wasted language or redundancy, making it easy for an agent to parse quickly. Every word earns its place by defining the core function without unnecessary elaboration.
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 complexity of a code refactoring tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., suggestions, examples, scores), behavioral traits like safety or limitations, or how it differs from siblings. For a tool that likely provides nuanced advice, more context is needed to help the agent use it effectively.
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 100%, with clear descriptions for all parameters ('code', 'language', 'focus'), including an enum for 'focus'. The description adds no additional semantic information beyond what's in the schema—it doesn't explain parameter interactions, default behaviors, or examples. Given the high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 purpose: '提供代码重构建议，改善代码结构和可读性' (Provide code refactoring suggestions to improve code structure and readability). It specifies the verb '提供建议' (provide suggestions) and the resource '代码' (code), with the goal of improving structure and readability. However, it doesn't explicitly distinguish this from sibling tools like 'analyze_code_quality' or 'generate_documentation', which might have overlapping purposes.
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 doesn't mention sibling tools like 'analyze_code_quality' (which might analyze without suggesting refactoring) or 'generate_documentation' (which focuses on documentation). There's no context on prerequisites, limitations, or specific scenarios where this tool is preferred over others, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![mcp-code MCP server](https://glama.ai/mcp/servers/renjismzy/mcp-code/badges/card.svg)](https://glama.ai/mcp/servers/renjismzy/mcp-code)

Score Badge

Copy to your README.md:

[![mcp-code MCP server](https://glama.ai/mcp/servers/renjismzy/mcp-code/badges/score.svg)](https://glama.ai/mcp/servers/renjismzy/mcp-code)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/renjismzy/mcp-code'

If you have feedback or need assistance with the MCP directory API, please join our Discord server