Python Code Review MCP Agent

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
Multiple tools have unclear boundaries and overlapping purposes. 'analyze_code_quality' and 'review_python_code' both describe comprehensive code analysis with quality and security focus, making them highly ambiguous. 'get_improvement_suggestions' also overlaps with these by providing actionable recommendations, while 'security_audit' is a subset of their security aspects. This will likely cause agent misselection.
Naming Consistency4/5
The naming follows a consistent verb_noun pattern with snake_case throughout, such as 'analyze_code_quality' and 'compare_code_versions'. All tools start with a verb and describe their function clearly, with no mixing of conventions. The consistency aids in readability and predictability.
Tool Count4/5
With 5 tools, the count is reasonable for a Python code review domain, allowing focused operations without being overwhelming. It aligns well with typical MCP server scopes of 3-15 tools, though the overlap in functionality might suggest some tools could be consolidated for better efficiency.
Completeness3/5
The tool set covers core aspects of code review like quality analysis, security, and suggestions, but there are notable gaps. For example, there is no tool for generating summaries, integrating with version control, or handling specific Python frameworks, which could limit agent workflows. The coverage is functional but not fully comprehensive for a code review agent.
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
- No commit activity data available
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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 burden for behavioral disclosure. While it mentions what aspects are analyzed, it doesn't describe important behavioral traits like whether this is a read-only operation, what the output format looks like, whether there are rate limits, computational costs, or specific authentication requirements. For a tool with 4 parameters and no annotations, this is a significant gap.
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 that communicates the core purpose without unnecessary words. It's appropriately sized and front-loaded with the main action and scope, making it easy for an agent to quickly understand what the tool does.
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?
For a tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the analysis returns, how results are structured, what happens with the optional parameters, or how this differs from similar sibling tools. The description should provide more context given the complexity and lack of structured metadata.
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 fully documents all 4 parameters. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.
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 performs 'Deep code quality analysis' and specifies the aspects covered: style, maintainability, performance, and best practices compliance. This provides a specific verb (analyze) and resource (code quality) with scope details, though it doesn't explicitly differentiate from sibling tools like 'review_python_code' or 'security_audit'.
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 sibling tools like 'review_python_code', 'security_audit', and 'get_improvement_suggestions' available, there's no indication of when this comprehensive analysis is preferred over more specialized tools or what prerequisites might exist.
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 compares code for quality and security, but doesn't explain how this comparison works, what metrics are used, whether it's read-only or has side effects, or what the output format is. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and scope, making it easy to parse. Every part of the sentence contributes essential information, earning its place.
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 code comparison and the lack of annotations and output schema, the description is insufficiently complete. It doesn't detail the comparison methodology, output format, or behavioral traits like error handling. For a tool with no structured data beyond the input schema, more context is needed to guide effective agent usage.
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 (originalCode, revisedCode, filename). The description adds no additional semantic context beyond what the schema provides, such as explaining how the comparison uses these inputs or what 'filename' influences. With high schema coverage, the baseline score of 3 is appropriate as the schema handles parameter documentation adequately.
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: comparing two Python code versions to identify improvements or regressions in quality and security. It specifies the verb 'compare' and resource 'Python code versions' with the goal of evaluating quality and security. However, it doesn't explicitly differentiate from sibling tools like 'analyze_code_quality' or 'security_audit', which might have overlapping functionality.
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 mentions identifying improvements or regressions, but doesn't specify contexts, prerequisites, or exclusions. With sibling tools like 'analyze_code_quality' and 'security_audit' available, the lack of differentiation leaves the agent uncertain 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool provides 'specific, actionable suggestions' but doesn't describe how suggestions are generated, whether they include code examples, if there are rate limits, or what the output format looks like. This is a significant gap for a tool that analyzes code without output schema details.
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 that front-loads the core purpose without unnecessary words. Every part of the sentence contributes to understanding the tool's function, making it appropriately concise and well-structured.
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 code analysis, lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like suggestion depth, error handling, or output format, which are crucial for an AI agent to use this tool effectively in context with its siblings.
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 parameter-specific information beyond what's in the schema, which has 100% coverage with clear descriptions for all parameters. The baseline score of 3 is appropriate since the schema adequately documents parameters, but the description doesn't enhance understanding of how parameters like 'focusArea' affect the suggestions.
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 with specific verbs ('get suggestions') and resources ('Python code quality, security, and maintainability'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'analyze_code_quality' or 'review_python_code', which might have overlapping functionality.
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 'security_audit' or 'compare_code_versions'. It mentions 'focusArea' in the schema but doesn't explain in the description when to choose specific focus areas or when this tool is preferred over siblings, leaving usage context implied at best.
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 mentions 'detailed reports with actionable recommendations,' which hints at output format, but lacks critical details like whether this is a read-only analysis, potential performance impacts, error handling, or authentication needs. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Comprehensive Python code analysis focusing on quality and security') and adds a secondary detail ('Provides detailed reports with actionable recommendations'). It avoids redundancy and wastes no words, though it could be slightly more structured for clarity.
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 moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and output type but lacks details on behavioral traits, usage guidelines, and differentiation from siblings. Without annotations or output schema, more context on what the reports contain or how to interpret results would improve completeness.
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 parameters (code, filename, reportType) with descriptions and defaults. The description adds no additional meaning beyond what the schema provides, such as explaining how 'reportType' affects the analysis or providing examples. Baseline 3 is appropriate when the schema handles parameter documentation adequately.
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 as 'Comprehensive Python code analysis focusing on quality and security' with 'detailed reports with actionable recommendations.' This specifies the verb (analyze), resource (Python code), and scope (quality and security). However, it doesn't explicitly differentiate from sibling tools like 'analyze_code_quality' or 'security_audit,' which likely have overlapping functionality.
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 'security_audit.' It mentions 'quality and security' but doesn't specify contexts, exclusions, or prerequisites. Usage is implied through the purpose statement, but explicit alternatives or conditions are missing.
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 (analyzes for security vulnerabilities) but doesn't describe how it behaves: no information about output format, whether it's a read-only analysis or modifies code, performance characteristics, error handling, or any limitations. For a security analysis tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.
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 - just two sentences that directly state the tool's purpose and scope. Every word earns its place: 'Focused security vulnerability analysis for Python code' establishes the core function, and 'Identifies SQL injection, command injection, and other security risks' provides concrete examples without unnecessary elaboration. It's front-loaded with the most important 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 that this is a security analysis tool with no annotations and no output schema, the description is insufficiently complete. While concise, it doesn't explain what the tool returns (e.g., a vulnerability report, risk scores, remediation suggestions), how comprehensive the analysis is, what types of 'other security risks' it covers, or any limitations. For a tool that presumably produces important security findings, more context about outputs and scope would be valuable.
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 thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions 'Python code to audit' which aligns with the 'code' parameter description, but provides no additional context about parameter usage, constraints, or interactions. This meets the baseline expectation when schema coverage is complete.
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: 'Focused security vulnerability analysis for Python code' with specific examples of what it identifies (SQL injection, command injection, and other security risks). It distinguishes itself from siblings by focusing specifically on security vulnerabilities rather than general code quality, version comparison, or improvement suggestions. However, it doesn't explicitly contrast with the 'review_python_code' sibling, which might have some overlap.
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. While it mentions 'focused security vulnerability analysis,' it doesn't specify when to choose this over 'review_python_code' (which might include security aspects) or 'analyze_code_quality' (which might cover security as part of broader quality metrics). There's no mention of prerequisites, limitations, or typical use cases.
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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