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/tool-function-schema-designer

@63451c7

Designs robust function/tool calling schemas for LLMs with JSON schemas, validation strategies, typed interfaces, and example calls. Use when implementing "function calling", "tool use", "LLM tools", or "agent actions".

Use this Skill: https://skilld.dev/gh/patricio0312rev/skills/tool-function-schema-designer

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SKILL.md

≈63 tokens always: the name and description. ≈1.2k when used: this file.

Tool/Function Schema Designer

Design robust tool schemas that LLMs can reliably invoke.

Function Schema Format

// OpenAI function calling format
const searchDocsTool = {
  type: "function",
  function: {
    name: "search_documentation",
    description:
      "Search through product documentation using semantic search. Use this when the user asks about features, how-tos, or troubleshooting.",
    parameters: {
      type: "object",
      properties: {
        query: {
          type: "string",
          description: "The search query, phrased as a question or keywords",
        },
        filters: {
          type: "object",
          properties: {
            category: {
              type: "string",
              enum: ["api", "guides", "tutorials", "troubleshooting"],
              description: "Filter by documentation category",
            },
            version: {
              type: "string",
              description: "Filter by product version (e.g., 'v2.0')",
            },
          },
        },
        max_results: {
          type: "integer",
          minimum: 1,
          maximum: 10,
          default: 5,
          description: "Maximum number of results to return",
        },
      },
      required: ["query"],
    },
  },
};

Typed Interfaces

// TypeScript types matching schema
interface SearchDocsParams {
  query: string;
  filters?: {
    category?: "api" | "guides" | "tutorials" | "troubleshooting";
    version?: string;
  };
  max_results?: number;
}

// Implementation
async function search_documentation(
  params: SearchDocsParams
): Promise<SearchResult[]> {
  const { query, filters = {}, max_results = 5 } = params;

  // Implementation
  return await vectorStore.search(query, {
    filter: filters,
    limit: max_results,
  });
}

Validation Strategy

import { z } from "zod";

// Zod schema for runtime validation
const searchDocsSchema = z.object({
  query: z.string().min(1, "Query cannot be empty"),
  filters: z
    .object({
      category: z
        .enum(["api", "guides", "tutorials", "troubleshooting"])
        .optional(),
      version: z.string().optional(),
    })
    .optional(),
  max_results: z.number().int().min(1).max(10).default(5),
});

// Validate before execution
function validateAndExecute(toolName: string, params: unknown) {
  const validated = searchDocsSchema.parse(params);
  return search_documentation(validated);
}

Tool Registry

export const TOOLS = {
  search_documentation: {
    schema: searchDocsTool,
    implementation: search_documentation,
    validator: searchDocsSchema,
  },
  create_ticket: {
    schema: createTicketTool,
    implementation: create_ticket,
    validator: createTicketSchema,
  },
  // ... more tools
};

// Execute tool safely
async function executeTool(name: string, params: unknown) {
  const tool = TOOLS[name];
  if (!tool) throw new Error(`Unknown tool: ${name}`);

  const validated = tool.validator.parse(params);
  return tool.implementation(validated);
}

Example Calls

// Example 1: Simple search
{
  "name": "search_documentation",
  "parameters": {
    "query": "How do I authenticate API requests?"
  }
}

// Example 2: With filters
{
  "name": "search_documentation",
  "parameters": {
    "query": "rate limiting",
    "filters": {
      "category": "api",
      "version": "v2.0"
    },
    "max_results": 3
  }
}

Error Handling

interface ToolResult {
  success: boolean;
  data?: any;
  error?: {
    code: string;
    message: string;
  };
}

async function safeExecuteTool(
  name: string,
  params: unknown
): Promise<ToolResult> {
  try {
    const data = await executeTool(name, params);
    return { success: true, data };
  } catch (error) {
    if (error instanceof z.ZodError) {
      return {
        success: false,
        error: {
          code: "VALIDATION_ERROR",
          message: `Invalid parameters: ${error.message}`,
        },
      };
    }
    return {
      success: false,
      error: {
        code: "EXECUTION_ERROR",
        message: error.message,
      },
    };
  }
}

Best Practices

  1. Clear descriptions: Explain when to use the tool
  2. Specific types: Use enums, ranges, patterns
  3. Sensible defaults: Reduce required parameters
  4. Validate rigorously: Don't trust LLM output
  5. Error messages: Help LLM correct mistakes
  6. Example calls: Show success cases
  7. Type safety: TypeScript interfaces

Output Checklist

  • JSON schema defined
  • TypeScript interface
  • Validation with Zod
  • Implementation function
  • Error handling
  • Example calls (3+)
  • Tool registry entry
  • Documentation

Source: SKILL.md on GitHub

1 warning17d5 checks · Risk SAFE
  • Gen Agent Trust Hub17d

    This skill provides a set of templates and best practices for designing LLM tool schemas. It includes examples for JSON schemas, TypeScript interfaces, and runtime validation using Zod. No malicious patterns or security risks were detected.

  • Socket17d

    No alerts

  • Snyk17d

    Risk: LOW · No issues

  • Runlayer7mo

    1/1 file flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at 63451c7. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 months ago.

Dormantupdated 9 months ago

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