The Coding Standards MCP Server provides tools for accessing coding style guidelines and best practices for various programming languages including Java, Python, and React.
Access Style Guidelines: Retrieve Markdown-formatted coding style guides for supported languages
Access Best Practices: Get Markdown-formatted application best practices for supported technologies
List Templates: View all available templates grouped by type and language
Configuration: Easy MCP client setup using UV package manager
Development Mode: Run with MCP inspector on port 3000 for development
Provides access to Python coding style guidelines based on PEP 8 and best practices for project layout, dependency management, and testing.
Offers React best practices including component structure, hooks usage, component patterns, state management, performance optimization, and TypeScript integration.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Coding Standards MCP Server show me Python style guidelines for function naming"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This MCP server provides tools for accessing coding style guidelines and best practices for various technologies (Java, Python, React).
Python 3.8 or higher
MCP package (pip install mcp)
UV package manager (recommended)
Related MCP server: Best Practices MCP Server
Install the server in Claude:
mcp install server.pyRun in development mode:
mcp dev server.pyThe MCP inspector will start on port 3000.
Access language-specific coding style guidelines:
Java: Clean code practices, naming conventions, code organization
Python: PEP 8 based guidelines, Pythonic code practices
React: Component structure, hooks usage, TypeScript integration
Access language-specific application best practices:
Java: Project structure, architecture, testing, security
Python: Project layout, dependency management, testing practices
React: Component patterns, state management, performance optimization
java_style_guide: Get Java coding style guidelines
Returns: Markdown formatted style guide
Example: nortal_coding_standards_java_style_guide()
java_best_practices: Get Java application best practices
Returns: Markdown formatted best practices
Example: nortal_coding_standards_java_best_practices()
python_style_guide: Get Python coding style guidelines
Returns: Markdown formatted style guide
Example: nortal_coding_standards_python_style_guide()
python_best_practices: Get Python application best practices
Returns: Markdown formatted best practices
Example: nortal_coding_standards_python_best_practices()
react_best_practices: Get React application best practices
Returns: Markdown formatted best practices
Example: nortal_coding_standards_react_best_practices()
Add this to your MCP client configuration file (e.g., ~/.codeium/windsurf/mcp_config.json):
{
"mcpServers": {
"nortal_coding_standards": {
"command": "uv",
"args": [
"run",
"--with",
"mcp[cli]",
"mcp",
"run",
"/absolute/path/to/coding-standards-mcp/server.py"
]
}
}
}Replace /absolute/path/to/coding-standards-mcp/server.py with your actual server path.
This project is licensed under the MIT License - see the LICENSE file for details.
Get application best practices for the specified language in Markdown format
| Name | Required | Description | Default |
|---|---|---|---|
| language | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states what the tool does, not how it behaves. It doesn't disclose whether this is a read-only operation, if it requires authentication, rate limits, error conditions, or what happens if the language isn't supported. The description adds minimal behavioral context beyond the basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's appropriately sized for a simple tool and front-loads the core functionality without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (which handles return values), no annotations, and simple parameters, the description covers the basic purpose adequately. However, for a tool with 0% schema description coverage and no annotations, it should provide more parameter guidance and behavioral context to be truly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It mentions 'for the specified language' which maps to the single 'language' parameter, providing some semantic meaning. However, it doesn't specify format constraints, valid values, or examples for the language parameter, leaving significant gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('application best practices') with specific format ('Markdown format') and scoping ('for the specified language'). It doesn't explicitly differentiate from sibling tools like get_style_guide or list_templates, but the purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 get_style_guide or list_templates. It mentions the language parameter but doesn't explain prerequisites, limitations, or appropriate contexts for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get coding style guidelines for the specified language in Markdown format
| Name | Required | Description | Default |
|---|---|---|---|
| language | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 mentions the output format ('Markdown format') but doesn't disclose behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or error handling. For a tool with zero 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads key information (action, resource, format, scope) with zero waste. Every word earns its place, making it appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter) and the presence of an output schema, the description is reasonably complete. It covers the purpose and output format, though it lacks behavioral details and usage guidance. With annotations absent, it could do more, but the output schema mitigates some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, with one parameter ('language') undocumented in the schema. The description adds minimal semantics by implying the parameter specifies the language for guidelines, but it doesn't clarify allowed values, examples, or constraints. Baseline is 3 due to low coverage, but the description only partially compensates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get') and resource ('coding style guidelines'), specifying the format ('Markdown format') and scope ('for the specified language'). It distinguishes from sibling tools like 'get_best_practices' by focusing on style guidelines rather than broader practices, 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.
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 like 'get_best_practices' or 'list_templates'. The description implies usage for language-specific style guidelines but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available templates grouped by type and language
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 states the tool lists templates grouped by type and language, which is basic behavioral info, but lacks details on permissions, rate limits, pagination, or response format. For a tool with no annotations, this is a significant gap in transparency about how it behaves beyond the core action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that clearly states the purpose without any wasted words. It's front-loaded with the core action and includes essential details (grouping by type and language). Every part of the sentence earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no annotations, but has an output schema), the description is reasonably complete. It specifies what is listed and how it's grouped. With an output schema present, the description doesn't need to explain return values, so it covers the basics adequately, though it could benefit from more behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description adds no parameter info, which is appropriate here. Baseline for 0 parameters is 4, as it doesn't need to compensate for any gaps, and the description doesn't mislead about inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('templates'), specifying grouping by type and language. It distinguishes from siblings like 'get_best_practices' and 'get_style_guide' by focusing on templates rather than other content types. However, it doesn't explicitly mention what distinguishes it from potential template-related siblings (none listed here), so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 prerequisites, context for use, or compare to sibling tools like 'get_best_practices' or 'get_style_guide'. Usage is implied by the action of listing templates, but no explicit when/when-not instructions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_best_practicesget_style_guidelist_templatesEach tool has a clearly distinct purpose: get_best_practices focuses on application-level best practices, get_style_guide on coding style guidelines, and list_templates on available templates. There is no overlap in functionality, making tool selection unambiguous for an agent.
All tool names follow a consistent verb_noun pattern (get_best_practices, get_style_guide, list_templates) with clear, descriptive verbs and nouns. The naming is uniform and predictable across all three tools.
With only 3 tools, the server feels thin for a coding standards domain, which might include operations like validating code, applying templates, or managing standards. While the tools are well-defined, the count is borderline low for the apparent scope.
The tool surface has significant gaps for a coding standards server. It lacks CRUD operations for managing standards (e.g., create/update/delete practices or templates), validation tools, or integration with code analysis, which are core to the domain. This will likely cause agent failures in comprehensive workflows.
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