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get_validation_help

Read-only

Returns detailed help for Ilograph diagram validation, covering common issues, syntax, and best practices to create valid diagrams.

Instructions

    Provides comprehensive help for Ilograph diagram validation.

    Returns guidance on common validation issues, proper syntax,
    and best practices for creating valid Ilograph diagrams.

    Returns:
        str: Detailed validation help in markdown format
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The async handler function for the 'get_validation_help' tool. It returns a hardcoded markdown string with comprehensive Ilograph diagram validation guidance, including overview, validation process, common issues, top-level properties, example structure, and tips for success.
        async def get_validation_help(ctx: Context) -> str:
            """
            Provides comprehensive help for Ilograph diagram validation.
    
            Returns guidance on common validation issues, proper syntax,
            and best practices for creating valid Ilograph diagrams.
    
            Returns:
                str: Detailed validation help in markdown format
            """
            try:
                await ctx.info("Providing Ilograph validation help")
    
                help_content = """# Ilograph Diagram Validation Help
    
    ## Overview
    This validation tool checks your Ilograph diagrams for both YAML syntax correctness and Ilograph-specific schema compliance.
    
    ## Validation Process
    1. **YAML Syntax Check**: Ensures your diagram is valid YAML
    2. **Schema Validation**: Checks Ilograph-specific structure and properties
    3. **Best Practice Suggestions**: Provides recommendations for improvement
    
    ## Common Issues and Solutions
    
    ### YAML Syntax Errors
    - **Indentation**: Use consistent spaces (2 or 4), not tabs
    - **Missing Colons**: Properties need colons (`name: value`)
    - **List Format**: Use dashes for lists or bracket notation
    - **Quoted Strings**: Quote strings with special characters
    
    ### Ilograph Schema Issues
    - **Missing Required Properties**: Resources need `name` or `id`
    - **Unknown Properties**: Check property names against specification
    - **Duplicate IDs**: Resource IDs must be unique
    - **Invalid Relations**: Relations need both `from` and `to`
    
    ## Valid Top-Level Properties
    - `resources`: Your diagram components (required for meaningful diagrams)
    - `perspectives`: Different views of your architecture
    - `contexts`: Multiple context views
    - `imports`: External namespace imports
    - `layout`: Diagram layout properties
    
    ## Example Valid Structure
    ```yaml
    imports:
    - from: ilograph/aws
      namespace: AWS
    
    resources:
    - name: Web Server
      subtitle: Frontend
      description: Serves the web application
      icon: server.svg
      children:
      - name: Load Balancer
        instanceOf: AWS::ElasticLoadBalancer
    
    perspectives:
    - name: System Overview
      relations:
      - from: User
        to: Web Server
        label: HTTPS requests
    ```
    
    ## Getting More Help
    - Use `fetch_spec_tool()` to get the complete Ilograph specification
    - Use `fetch_documentation_tool(section='tutorial')` for detailed tutorials
    - Use `fetch_example_tool()` to see working example diagrams
    
    ## Tips for Success
    1. Start with simple structures and build complexity gradually
    2. Use consistent naming conventions
    3. Add descriptions to improve documentation
    4. Validate frequently during development
    5. Check examples for pattern guidance
    """
    
                await ctx.info("Validation help provided successfully")
                return help_content
    
            except Exception as e:
                error_msg = f"Error providing validation help: {str(e)}"
                await ctx.error(error_msg)
                return f"Error: {error_msg}"
  • The @mcp.tool decorator that registers 'get_validation_help' as a FastMCP tool with title 'Get Validation Help' and readOnlyHint annotation.
    @mcp.tool(
        annotations={
            "title": "Get Validation Help",
            "readOnlyHint": True,
            "description": "Provides guidance on Ilograph diagram validation and common issues",
        }
    )
  • The call to register_validate_diagram_tool(mcp) in the server setup, which registers both validate_diagram_tool and get_validation_help tools.
    register_validate_diagram_tool(mcp)
    logger.info("Registered validate_diagram_tool")
  • Helper function get_tool_info() that reports 'get_validation_help' as one of the tools provided by the validate_diagram_tool module.
    def get_tool_info() -> dict:
        """Get information about the validation tools for registration."""
        return {
            "name": "validate_diagram_tool",
            "description": "Comprehensive Ilograph diagram validation with detailed error reporting",
            "tools": [
                "validate_diagram_tool",
                "get_validation_help",
            ],
        }
  • Reference to 'get_validation_help' in the server instructions/description string shown to users.
    - get_validation_help: Provides guidance on Ilograph diagram validation and common issues
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true. The description adds that the tool returns a markdown string, which is useful context, but does not disclose any other behavioral traits beyond what annotations provide.

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 concise, front-loaded with the purpose, and includes a Returns section. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

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

Given no parameters and no output schema, the description fully covers the tool's behavior. It explains the purpose, the type of help, and the output format, which is sufficient for an agent to use it correctly.

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 input schema has no parameters, so schema coverage is 100%. The description adds no parameter info, which is appropriate. Baseline 4 applies.

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

Purpose5/5

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

The description clearly states it provides comprehensive help for Ilograph diagram validation, including guidance on common issues, syntax, and best practices. It distinguishes itself from sibling tools like validate_diagram_tool by focusing on help rather than validation itself.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for validation help but does not explicitly state when to use it versus alternatives such as validate_diagram_tool or check_documentation_health. No exclusions or comparisons are provided.

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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