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get_cv_guidelines

Access CV formatting guidelines and constraints to structure resumes correctly. This tool provides specifications for creating professional CVs with proper layout and content organization.

Instructions

Get CV formatting guidelines and constraints

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'get_cv_guidelines' tool. It returns a formatted string containing CV formatting guidelines and constraints as TextContent.
    async def get_cv_guidelines() -> list[TextContent]:
        """Get CV formatting guidelines."""
        guidelines = f"""CV Formatting Guidelines:
    
    IMPORTANT: When generating or updating CV content, follow these rules:
    
    1. **Maximum Bullet Points**: {MAX_BULLETS_PER_EXPERIENCE} bullet points per experience/role
    2. **Focus on Impact**: Prioritize achievements with quantifiable results
    3. **Format**: Use \\textbf{{}} for emphasis on key terms
    4. **Metrics**: Include specific numbers, percentages, or time savings
    5. **Action Verbs**: Start each bullet with strong action verbs (Engineered, Designed, Implemented, etc.)
    6. **Relevance**: Select the most impactful and recent achievements
    
    When asked to update a CV:
    - Analyze all available data (git commits, Jira tickets, Credly badges, wins)
    - Identify the top {MAX_BULLETS_PER_EXPERIENCE} most significant achievements
    - Format them as LaTeX bullet points
    - Ensure each bullet demonstrates clear business value
    
    Configuration:
    - MAX_BULLETS_PER_EXPERIENCE: {MAX_BULLETS_PER_EXPERIENCE}
    - This can be customized via MAX_BULLETS_PER_EXPERIENCE environment variable"""
        
        return [TextContent(type="text", text=guidelines)]
  • Registration of the 'get_cv_guidelines' tool in the MCP app's tools list, including name, description, and empty input schema.
    Tool(
        name="get_cv_guidelines",
        description="Get CV formatting guidelines and constraints",
        inputSchema={
            "type": "object",
            "properties": {}
        }
    ),
  • Input schema for the 'get_cv_guidelines' tool, which requires no parameters (empty properties).
    inputSchema={
        "type": "object",
        "properties": {}
    }
  • Dispatch logic in the main call_tool handler that invokes the get_cv_guidelines function when the tool name matches.
    elif name == "get_cv_guidelines":
        return await get_cv_guidelines()
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 only states what the tool does without mentioning any traits like whether it's read-only, requires authentication, has rate limits, or what the output format might be. This is a significant gap for a tool with zero annotation coverage.

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 directly states the tool's purpose without any wasted words. It is front-loaded and appropriately sized for a simple tool, 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.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'guidelines and constraints' entail, the format of the return value, or any behavioral aspects like error handling. For a tool with no structured data to rely on, more context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter details, and it appropriately doesn't mention any, earning a high baseline score for this dimension.

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 with a specific verb ('Get') and resource ('CV formatting guidelines and constraints'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'parse_cv_pdf' or 'read_cv', which also deal with CVs but serve different functions, so it doesn't reach the highest score.

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 provides no guidance on when to use this tool versus alternatives. With sibling tools such as 'parse_cv_pdf' and 'read_cv' that handle CV-related tasks, there's no indication of context, prerequisites, or exclusions, leaving usage ambiguous.

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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