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MCP Server Template for Cursor IDE

by jankowtf

apply_prompt_proceed

Generate structured prompts to continue tasks or projects in Cursor IDE by providing task descriptions and optional instructions.

Instructions

Provides a prompt template for proceeding with a task or project

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesA description of the task or project to proceed with
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 for the 'apply_prompt_proceed' tool. It renders a Jinja2 prompt template named 'proceed' using the input task, optional specific_instructions, and version, returning the rendered text as TextContent.
    async def apply_prompt_proceed(
        task: str,
        specific_instructions: str = "",
        version: str = "latest",
    ) -> list[types.TextContent]:
        """
        Provides a prompt template for proceeding with a task or project.
    
        Args:
            task: A description of the task or project to proceed with.
            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 task description and specific instructions
        response_text = render_prompt_template(
            "proceed",
            version_str=version,
            task=task,
            specific_instructions=specific_instructions,
        )
        return [types.TextContent(type="text", text=response_text)]
  • Tool registration in the list_tools() function, including the name, description, and inputSchema defining the tool's parameters.
    types.Tool(
        name="apply_prompt_proceed",
        description="Provides a prompt template for proceeding with a task or project",
        inputSchema={
            "type": "object",
            "required": ["task"],
            "properties": {
                "task": {
                    "type": "string",
                    "description": "A description of the task or project to proceed with",
                },
                "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')",
                },
            },
        },
    ),
  • Dispatch logic in the generic @app.call_tool() handler that validates inputs and calls the specific apply_prompt_proceed function.
    elif name == "apply_prompt_proceed":
        if "task" not in arguments:
            return [
                types.TextContent(
                    type="text", text="Error: Missing required argument 'task'"
                )
            ]
        version = arguments.get("version", "latest")
        specific_instructions = arguments.get("specific_instructions", "")
        return await apply_prompt_proceed(
            arguments["task"],
            specific_instructions=specific_instructions,
            version=version,
        )
  • Helper function used by the handler to load, resolve version, parse metadata, and render the specific Jinja2 prompt template (invoked with template_name='proceed').
    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 providing a prompt template but doesn't disclose behavioral traits such as whether it generates, modifies, or retrieves templates, what the output format is, or any constraints like rate limits or permissions needed. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with no wasted words. It's appropriately sized and front-loaded, efficiently stating the tool's purpose without unnecessary elaboration, making it easy to parse quickly.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a string template, structured data), how it should be used in workflows, or any side effects. For a tool with 3 parameters and behavioral uncertainty, more context is needed to be fully helpful.

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 well. The description adds no additional meaning beyond the schema, such as explaining how parameters interact or providing examples. Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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 proceeding with a task or project', which gives a general purpose but lacks specificity. It doesn't clearly distinguish from siblings like 'apply_prompt_initial' or 'apply_prompt_change', leaving ambiguity about what 'proceeding' means versus 'initial' or 'change' prompts.

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. With siblings like 'apply_prompt_initial' and 'apply_prompt_change', the description offers no context on whether this is for ongoing tasks, specific phases, or how it differs from other prompt tools, leaving usage unclear.

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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