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ggerve

Coding Standards MCP Server

by ggerve

get_style_guide

Retrieve coding style guidelines in Markdown format for a specified programming language to ensure consistent code quality and maintainability.

Instructions

Get coding style guidelines for the specified language in Markdown format

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • server.py:60-65 (handler)
    The main handler function for the 'get_style_guide' tool. It is registered via the @mcp.tool() decorator and implements the tool logic by reading and returning the corresponding style guide template file for the given language.
    @mcp.tool()
    def get_style_guide(language: str) -> str:
        """Get coding style guidelines for the specified language in Markdown format"""
    
        filename = f"{language}_style_guide.md"
        return read_template(filename)
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. 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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose4/5

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.

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 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.

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