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MCP Server Template for Cursor IDE

by jankowtf

apply_prompt_fix_linter

Analyzes linter errors and generates prompts to fix them in Cursor IDE, helping developers resolve coding issues efficiently.

Instructions

Provides a prompt for analyzing and fixing linter errors

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
issueYesA description of the linter errors to be analyzed and fixed
specific_instructionsNoOptional specific instructions to include in the prompt
versionNoThe version of the prompt template to use (e.g., '1.0.0', '1.1.0', or 'latest')

Implementation Reference

  • The handler function that executes the tool logic by rendering a specific prompt template ('fix_linter') for fixing linter issues.
    async def apply_prompt_fix_linter(
        issue: str,
        specific_instructions: str = "",
        version: str = "latest",
    ) -> list[types.TextContent]:
        """
        Provides a prompt for analyzing and fixing linter errors.
    
        Args:
            issue: A description of the linter errors to be analyzed and fixed.
            specific_instructions: Optional specific instructions to include in the prompt.
            version: The version of the prompt template to use. Defaults to "latest".
    
        Returns:
            A list containing a TextContent object with the prompt.
        """
        # Render the prompt template with the issue and specific instructions
        response_text = render_prompt_template(
            "fix_linter",
            version_str=version,
            issue=issue,
            specific_instructions=specific_instructions,
        )
        return [types.TextContent(type="text", text=response_text)]
  • The input schema definition for the 'apply_prompt_fix_linter' tool, defining parameters like issue, specific_instructions, and version.
    types.Tool(
        name="apply_prompt_fix_linter",
        description="Provides a prompt for analyzing and fixing linter errors",
        inputSchema={
            "type": "object",
            "required": ["issue"],
            "properties": {
                "issue": {
                    "type": "string",
                    "description": "A description of the linter errors to be analyzed and fixed",
                },
                "specific_instructions": {
                    "type": "string",
                    "description": "Optional specific instructions to include in the prompt",
                },
                "version": {
                    "type": "string",
                    "description": "The version of the prompt template to use (e.g., '1.0.0', '1.1.0', or 'latest')",
                },
            },
        },
    ),
  • The dispatch/registration logic within the @app.call_tool() handler that routes calls to the apply_prompt_fix_linter function when the tool name matches.
    elif name == "apply_prompt_fix_linter":
        if "issue" not in arguments:
            return [
                types.TextContent(
                    type="text", text="Error: Missing required argument 'issue'"
                )
            ]
        version = arguments.get("version", "latest")
        specific_instructions = arguments.get("specific_instructions", "")
        return await apply_prompt_fix_linter(
            issue=arguments["issue"],
            specific_instructions=specific_instructions,
            version=version,
        )
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 a prompt,' implying it generates or returns text, but doesn't specify if this involves network calls, rate limits, authentication needs, or output format. For a tool with no annotation coverage, this leaves critical behavioral traits undocumented, though it doesn't contradict any annotations.

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: 'Provides a prompt for analyzing and fixing linter errors.' It is front-loaded with the core purpose, avoids redundancy, and uses minimal words to convey the essential function. Every word 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.

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 states what the tool does but lacks details on behavioral traits, usage context, or output expectations. Without annotations or an output schema, more completeness would be beneficial, but it meets a basic threshold for understanding the tool's purpose.

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, documenting all three parameters (issue, specific_instructions, version) clearly. The description adds no additional meaning beyond the schema, such as examples or context for parameter use. With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract from the schema's documentation.

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: 'Provides a prompt for analyzing and fixing linter errors.' It specifies the verb ('provides a prompt') and resource ('analyzing and fixing linter errors'), making the intent unambiguous. However, it doesn't distinguish this tool from its siblings (e.g., apply_prompt_fix, apply_prompt_change), which share similar naming patterns but likely differ in focus, so it misses full 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?

The description offers no guidance on when to use this tool versus alternatives. It lacks explicit instructions on context, prerequisites, or comparisons with sibling tools like apply_prompt_fix or apply_prompt_unit_tests. Without such details, users must infer usage from the name alone, which is insufficient for effective tool selection.

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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