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agilesix

VA Form Generation MCP Server

by agilesix

get_agent_prompt

Retrieve specialized prompts for VA form auditing agents to validate and fix scaffolded forms according to VA.gov content standards.

Instructions

Get the specialized prompt for a specific agent (1-4)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_numberYesAgent number (1-4)
form_numberYesVA form number to customize prompt
form_pathYesPath to form directory
pdf_pathNoPath to PDF file

Implementation Reference

  • Handler implementation for the get_agent_prompt tool, which retrieves a base agent prompt and replaces template placeholders with form-specific details.
    case 'get_agent_prompt': {
      const { agent_number, form_number, form_path, pdf_path } = args;
    
      const basePrompt = await loadAgentPrompt(agent_number);
    
      // Customize prompt with form-specific info
      const customizedPrompt = basePrompt
        .replace(/\[FORM-NUMBER\]/g, form_number)
        .replace(/\[FORM-PATH\]/g, form_path)
        .replace(/\[PDF-PATH\]/g, pdf_path || '[PDF not specified]');
    
      return {
        content: [
          {
            type: 'text',
            text: customizedPrompt,
          },
        ],
      };
    }
  • index.js:71-96 (registration)
    Registration and schema definition for the get_agent_prompt tool.
      name: 'get_agent_prompt',
      description: 'Get the specialized prompt for a specific agent (1-4)',
      inputSchema: {
        type: 'object',
        properties: {
          agent_number: {
            type: 'number',
            description: 'Agent number (1-4)',
            enum: [1, 2, 3, 4],
          },
          form_number: {
            type: 'string',
            description: 'VA form number to customize prompt',
          },
          form_path: {
            type: 'string',
            description: 'Path to form directory',
          },
          pdf_path: {
            type: 'string',
            description: 'Path to PDF file',
          },
        },
        required: ['agent_number', 'form_number', 'form_path'],
      },
    },
  • Helper function that reads the base agent prompt file from the templates directory.
    async function loadAgentPrompt(agentNumber) {
      const promptPath = path.join(__dirname, 'templates', `agent-${agentNumber}-prompt.md`);
      return await fs.readFile(promptPath, 'utf-8');
    }
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. It states it 'gets' a prompt, implying a read operation, but doesn't disclose behavioral traits such as whether it requires authentication, has rate limits, returns structured data, or handles errors. The description is minimal and misses key operational details.

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 unnecessary words. It is front-loaded and every part contributes to understanding the action and scope, 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 no annotations and no output schema, the description is incomplete. It lacks information on what the tool returns (e.g., prompt text, format), error handling, or any behavioral context. For a tool with 4 parameters and no structured output, more detail is needed to guide effective use.

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 fully documents all parameters. The description adds no additional meaning beyond implying the agent number is 1-4, which is already covered by the enum in the schema. Baseline score of 3 is appropriate as the schema handles parameter documentation effectively.

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 action ('Get') and resource ('specialized prompt for a specific agent'), specifying the agent number range (1-4). It distinguishes from sibling tools like 'audit_form' or 'validate_form' by focusing on prompt retrieval rather than form validation or auditing. However, it doesn't explicitly differentiate from 'generate_orchestration_prompt' which might also involve 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 like 'generate_orchestration_prompt' or other sibling tools. The description implies usage when needing a prompt for a specific agent, but lacks context on prerequisites, dependencies, or scenarios where this is preferred over other methods.

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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