Skip to main content
Glama
jankowtf

MCP Server Template for Cursor IDE

by jankowtf

apply_prompt_change

Generate structured prompts to implement change requests in Cursor IDE, providing systematic guidance for code modifications.

Instructions

Provides a prompt for systematically handling change requests

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
change_requestYesDescription of the change request to implement
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 executes the tool logic: renders the 'change' prompt template using render_prompt_template with the provided change_request and optional parameters, then returns it as MCP TextContent.
    async def apply_prompt_change(
        change_request: str,
        specific_instructions: str = "",
        version: str = "latest",
    ) -> list[types.TextContent]:
        """
        Provides a prompt for systematically handling change requests.
    
        Args:
            change_request: Description of the change request to implement.
            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 change request and specific instructions
        response_text = render_prompt_template(
            "change",
            version_str=version,
            change_request=change_request,
            specific_instructions=specific_instructions,
        )
        return [types.TextContent(type="text", text=response_text)]
  • The input schema definition for the apply_prompt_change tool, registered in the list_tools handler. Defines required 'change_request' and optional 'specific_instructions' and 'version' parameters.
    types.Tool(
        name="apply_prompt_change",
        description="Provides a prompt for systematically handling change requests",
        inputSchema={
            "type": "object",
            "required": ["change_request"],
            "properties": {
                "change_request": {
                    "type": "string",
                    "description": "Description of the change request to implement",
                },
                "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 registration and dispatching logic within the @app.call_tool() handler that checks arguments and invokes the apply_prompt_change handler function.
    elif name == "apply_prompt_change":
        if "change_request" not in arguments:
            return [
                types.TextContent(
                    type="text",
                    text="Error: Missing required argument 'change_request'",
                )
            ]
        version = arguments.get("version", "latest")
        specific_instructions = arguments.get("specific_instructions", "")
        return await apply_prompt_change(
            change_request=arguments["change_request"],
            specific_instructions=specific_instructions,
            version=version,
        )
  • The supporting utility function called by the handler to load, resolve version, parse metadata, and render the Jinja2 prompt template from prompts/change/*.md files.
    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?

With no annotations provided, the description carries full burden for behavioral disclosure. It only states that the tool 'provides a prompt' without explaining what format the prompt takes, whether it's interactive, what permissions are needed, or what happens after the prompt is provided. This leaves significant behavioral gaps for a tool with 3 parameters.

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 at just one sentence with no wasted words. It's front-loaded with the core purpose and efficiently communicates the basic function without unnecessary elaboration.

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 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool actually produces (just 'provides a prompt'), how that prompt should be used, or what the expected outcome is. The lack of behavioral context makes it inadequate for proper tool selection.

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 3 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter documentation.

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 for systematically handling change requests', which gives a general purpose but lacks specificity about what kind of prompt it provides or how it differs from sibling tools like apply_prompt_fix or apply_prompt_unit_tests. It doesn't clearly distinguish itself from other prompt-related tools in the server.

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 about when to use this tool versus alternatives like apply_prompt_fix or apply_prompt_unit_tests. The description mentions 'systematically handling change requests' but doesn't specify what types of change requests or contexts it's designed for, leaving the agent with no usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jankowtf/mcp-hitchcode'

If you have feedback or need assistance with the MCP directory API, please join our Discord server