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MCP Server Template for Cursor IDE

by jankowtf

apply_prompt_infra

Generate structured prompts for documenting system infrastructure and tool stacks in Cursor IDE, enabling clear technical specifications and implementation guidance.

Instructions

Provides a prompt template for laying out system infrastructure and tool stack information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
infrastructure_infoYesDescription of the infrastructure and tool stack
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 core handler function that takes infrastructure_info, renders the 'infra' prompt template using render_prompt_template, and returns it as TextContent.
    async def apply_prompt_infra(
        infrastructure_info: str,
        specific_instructions: str = "",
        version: str = "latest",
    ) -> list[types.TextContent]:
        """
        Provides a prompt template for laying out system infrastructure and tool stack information.
    
        Args:
            infrastructure_info: Description of the infrastructure and tool stack.
            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 infrastructure info and specific instructions
        response_text = render_prompt_template(
            "infra",
            version_str=version,
            objective=infrastructure_info,
            specific_instructions=specific_instructions,
        )
        return [types.TextContent(type="text", text=response_text)]
  • Registration of the tool in the list_tools() function, including name, description, and input schema definition.
    types.Tool(
        name="apply_prompt_infra",
        description="Provides a prompt template for laying out system infrastructure and tool stack information",
        inputSchema={
            "type": "object",
            "required": ["infrastructure_info"],
            "properties": {
                "infrastructure_info": {
                    "type": "string",
                    "description": "Description of the infrastructure and tool stack",
                },
                "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')",
                },
            },
        },
    ),
  • Input schema defining the parameters for the tool: required infrastructure_info, optional specific_instructions and version.
    inputSchema={
        "type": "object",
        "required": ["infrastructure_info"],
        "properties": {
            "infrastructure_info": {
                "type": "string",
                "description": "Description of the infrastructure and tool stack",
            },
            "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')",
            },
        },
  • Key helper function used by the handler to load the specific version of the 'infra' prompt template from the prompts directory and render it with Jinja2 using the provided variables.
    def render_prompt_template(
        template_name: str, version_str: str = "latest", **kwargs: Any
    ) -> str:
        """
        Render a prompt template with the given variables.
    
        Args:
            template_name: The name of the prompt template.
            version_str: The version of the template to use. Defaults to "latest".
            **kwargs: The variables to pass to the template.
    
        Returns:
            str: The rendered prompt template.
    
        Raises:
            FileNotFoundError: If the template file does not exist.
            jinja2.exceptions.TemplateError: If there is an error rendering the template.
            ValueError: If the specified version does not exist.
        """
        _build_version_registry()
    
        # Check if the template exists
        if template_name not in _version_registry:
            raise FileNotFoundError(f"Template not found: {template_name}")
    
        # Resolve the version
        if version_str == "latest":
            version_str = _version_registry[template_name].get("latest")
            if not version_str:
                raise ValueError(f"No versions found for template: {template_name}")
        elif version_str not in _version_registry[template_name]:
            # Try to find the closest version
            available_versions = get_template_versions(template_name)
            if not available_versions:
                raise ValueError(f"No versions found for template: {template_name}")
    
            # Find the highest version that is less than or equal to the requested version
            requested_ver = version.parse(version_str)
            for v in available_versions:
                if version.parse(v) <= requested_ver:
                    version_str = v
                    break
            else:
                # If no suitable version is found, use the oldest version
                version_str = available_versions[-1]
    
        # Get the filename from the registry
        filename = _version_registry[template_name][version_str]
    
        # Build the template path
        template_path = f"prompts/{template_name}/{filename}"
    
        # Load the template content
        content = load_template(template_path)
    
        # Parse the metadata and template content
        metadata, template_content = _parse_template_metadata(content)
    
        # Create a new template with just the content (without the front matter)
        env = get_template_env()
        template = env.from_string(template_content)
    
        # Render the template
        return template.render(**kwargs)
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. It mentions 'provides a prompt template', implying a read-only or informational operation, but doesn't disclose behavioral traits such as whether it modifies data, requires authentication, has rate limits, or what the output looks like. This leaves significant gaps in understanding how the tool behaves.

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 directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, though it could be slightly more informative to earn a higher score.

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 has 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how the prompt template is applied, or any behavioral context, making it inadequate for a tool with this complexity and lack of structured data.

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 ('infrastructure_info', 'specific_instructions', 'version') with descriptions. The tool description adds no additional meaning beyond what the schema provides, such as examples or context for parameter use, resulting in a baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'Provides a prompt template for laying out system infrastructure and tool stack information', which gives a general purpose but lacks specificity. It mentions a verb ('provides') and resource ('prompt template'), but doesn't distinguish from siblings like 'apply_prompt_docker' or 'apply_prompt_fix', making it vague about its unique function.

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. The description doesn't mention any context, prerequisites, or exclusions, and with multiple sibling tools (e.g., 'apply_prompt_docker', 'apply_prompt_fix'), there's no indication of when this specific prompt template is appropriate.

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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