1212 MCP Tool Design 54c4e772

Core principle: Whatever a user can do, the agent should be able to do. Don't artificially limit the agent—give it the same primitives a power user would have.

Wrong approach: Tools that encode business logic

tool("process_feedback", {
  feedback: z.string(),
  category: z.enum(["bug", "feature", "question"]),
  priority: z.enum(["low", "medium", "high"]),
}, async ({ feedback, category, priority }) => {
  // Tool decides how to process
  const processed = categorize(feedback);
  const stored = await saveToDatabase(processed);
  const notification = await notify(priority);
  return { processed, stored, notification };
});

Right approach: Primitives that enable any workflow

tool("store_item", {
  key: z.string(),
  value: z.any(),
}, async ({ key, value }) => {
  await db.set(key, value);
  return { text: `Stored ${key}` };
});

tool("send_message", {
  channel: z.string(),
  content: z.string(),
}, async ({ channel, content }) => {
  await messenger.send(channel, content);
  return { text: "Sent" };
});

The agent decides categorization, priority, and when to notify based on the system prompt.

Names should describe the capability, not the use case:

The prompt tells the agent when to use primitives. The tool just provides capability.

Tools accept data. They don't accept decisions.

Wrong: Tool accepts decisions

tool("format_content", {
  content: z.string(),
  format: z.enum(["markdown", "html", "json"]),
  style: z.enum(["formal", "casual", "technical"]),
}, ...)

Right: Tool accepts data, agent decides format

tool("write_file", {
  path: z.string(),
  content: z.string(),
}, ...)
// Agent decides to write index.html with HTML content, or data.json with JSON

Return enough information for the agent to verify and iterate.

Wrong: Minimal output

async ({ key }) => {
  await db.delete(key);
  return { text: "Deleted" };
}

Right: Rich output

async ({ key }) => {
  const existed = await db.has(key);
  if (!existed) {
    return { text: `Key ${key} did not exist` };
  }
  await db.delete(key);
  return { text: `Deleted ${key}. ${await db.count()} items remaining.` };
}

import { createSdkMcpServer, tool } from "@anthropic-ai/claude-agent-sdk";
import { z } from "zod";

export const serverName = createSdkMcpServer({
  name: "server-name",
  version: "1.0.0",
  tools: [
    // READ operations
    tool(
      "read_item",
      "Read an item by key",
      { key: z.string().describe("Item key") },
      async ({ key }) => {
        const item = await storage.get(key);
        return {
          content: [{
            type: "text",
            text: item ? JSON.stringify(item, null, 2) : `Not found: ${key}`,
          }],
          isError: !item,
        };
      }
    ),

    tool(
      "list_items",
      "List all items, optionally filtered",
      {
        prefix: z.string().optional().describe("Filter by key prefix"),
        limit: z.number().default(100).describe("Max items"),
      },
      async ({ prefix, limit }) => {
        const items = await storage.list({ prefix, limit });
        return {
          content: [{
            type: "text",
            text: `Found ${items.length} items:\n${items.map(i => i.key).join("\n")}`,
          }],
        };
      }
    ),

    // WRITE operations
    tool(
      "store_item",
      "Store an item",
      {
        key: z.string().describe("Item key"),
        value: z.any().describe("Item data"),
      },
      async ({ key, value }) => {
        await storage.set(key, value);
        return {
          content: [{ type: "text", text: `Stored ${key}` }],
        };
      }
    ),

    tool(
      "delete_item",
      "Delete an item",
      { key: z.string().describe("Item key") },
      async ({ key }) => {
        const existed = await storage.delete(key);
        return {
          content: [{
            type: "text",
            text: existed ? `Deleted ${key}` : `${key} did not exist`,
          }],
        };
      }
    ),

    // EXTERNAL operations
    tool(
      "call_api",
      "Make an HTTP request",
      {
        url: z.string().url(),
        method: z.enum(["GET", "POST", "PUT", "DELETE"]).default("GET"),
        body: z.any().optional(),
      },
      async ({ url, method, body }) => {
        const response = await fetch(url, { method, body: JSON.stringify(body) });
        const text = await response.text();
        return {
          content: [{
            type: "text",
            text: `${response.status} ${response.statusText}\n\n${text}`,
          }],
          isError: !response.ok,
        };
      }
    ),
  ],
});

This server provides primitives for storing feedback. It does NOT decide how to categorize or organize feedback—that's the agent's job via the prompt.

export const feedbackMcpServer = createSdkMcpServer({
  name: "feedback",
  version: "1.0.0",
  tools: [
    tool(
      "store_feedback",
      "Store a feedback item",
      {
        item: z.object({
          id: z.string(),
          author: z.string(),
          content: z.string(),
          importance: z.number().min(1).max(5),
          timestamp: z.string(),
          status: z.string().optional(),
          urls: z.array(z.string()).optional(),
          metadata: z.any().optional(),
        }).describe("Feedback item"),
      },
      async ({ item }) => {
        await db.feedback.insert(item);
        return {
          content: [{
            type: "text",
            text: `Stored feedback ${item.id} from ${item.author}`,
          }],
        };
      }
    ),

    tool(
      "list_feedback",
      "List feedback items",
      {
        limit: z.number().default(50),
        status: z.string().optional(),
      },
      async ({ limit, status }) => {
        const items = await db.feedback.list({ limit, status });
        return {
          content: [{
            type: "text",
            text: JSON.stringify(items, null, 2),
          }],
        };
      }
    ),

    tool(
      "update_feedback",
      "Update a feedback item",
      {
        id: z.string(),
        updates: z.object({
          status: z.string().optional(),
          importance: z.number().optional(),
          metadata: z.any().optional(),
        }),
      },
      async ({ id, updates }) => {
        await db.feedback.update(id, updates);
        return {
          content: [{ type: "text", text: `Updated ${id}` }],
        };
      }
    ),
  ],
});

The system prompt then tells the agent how to use these primitives:

## Feedback Processing

When someone shares feedback:
1. Extract author, content, and any URLs
2. Rate importance 1-5 based on actionability
3. Store using feedback.store_feedback
4. If high importance (4-5), notify the channel

Use your judgment about importance ratings.

This pattern is specifically for agent-native apps where you want the agent to have full access to an external API—the same access a user would have. It follows the core agent-native principle: "Whatever the user can do, the agent can do."

If you're building a constrained agent with limited capabilities, static tool mapping may be intentional. But for agent-native apps integrating with HealthKit, HomeKit, GraphQL, or similar APIs:

Static Tool Mapping (Anti-pattern for Agent-Native): Build individual tools for each API capability. Always out of date, limits agent to only what you anticipated.

// ❌ Static: Every API type needs a hardcoded tool
tool("read_steps", async ({ startDate, endDate }) => {
  return healthKit.query(HKQuantityType.stepCount, startDate, endDate);
});

tool("read_heart_rate", async ({ startDate, endDate }) => {
  return healthKit.query(HKQuantityType.heartRate, startDate, endDate);
});

tool("read_sleep", async ({ startDate, endDate }) => {
  return healthKit.query(HKCategoryType.sleepAnalysis, startDate, endDate);
});

// When HealthKit adds glucose tracking... you need a code change

Dynamic Capability Discovery (Preferred): Build a meta-tool that discovers what's available, and a generic tool that can access anything.

// ✅ Dynamic: Agent discovers and uses any capability

// Discovery tool - returns what's available at runtime
tool("list_available_capabilities", async () => {
  const quantityTypes = await healthKit.availableQuantityTypes();
  const categoryTypes = await healthKit.availableCategoryTypes();

  return {
    text: `Available health metrics:\n` +
          `Quantity types: ${quantityTypes.join(", ")}\n` +
          `Category types: ${categoryTypes.join(", ")}\n` +
          `\nUse read_health_data with any of these types.`
  };
});

// Generic access tool - type is a string, API validates
tool("read_health_data", {
  dataType: z.string(),  // NOT z.enum - let HealthKit validate
  startDate: z.string(),
  endDate: z.string(),
  aggregation: z.enum(["sum", "average", "samples"]).optional()
}, async ({ dataType, startDate, endDate, aggregation }) => {
  // HealthKit validates the type, returns helpful error if invalid
  const result = await healthKit.query(dataType, startDate, endDate, aggregation);
  return { text: JSON.stringify(result, null, 2) };
});

When to Use Each Approach:

The agent-native default is Dynamic. Only use Static when you're intentionally limiting the agent's capabilities.

Complete Dynamic Pattern:

// 1. Discovery tool: What can I access?
tool("list_health_types", "Get available health data types") { _ in
    let store = HKHealthStore()

    let quantityTypes = HKQuantityTypeIdentifier.allCases.map { $0.rawValue }
    let categoryTypes = HKCategoryTypeIdentifier.allCases.map { $0.rawValue }
    let characteristicTypes = HKCharacteristicTypeIdentifier.allCases.map { $0.rawValue }

    return ToolResult(text: """
        Available HealthKit types:

        ## Quantity Types (numeric values)
        \(quantityTypes.joined(separator: ", "))

        ## Category Types (categorical data)
        \(categoryTypes.joined(separator: ", "))

        ## Characteristic Types (user info)
        \(characteristicTypes.joined(separator: ", "))

        Use read_health_data or write_health_data with any of these.
        """)
}

// 2. Generic read: Access any type by name
tool("read_health_data", "Read any health metric", {
    dataType: z.string().describe("Type name from list_health_types"),
    startDate: z.string(),
    endDate: z.string()
}) { request in
    // Let HealthKit validate the type name
    guard let type = HKQuantityTypeIdentifier(rawValue: request.dataType)
                     ?? HKCategoryTypeIdentifier(rawValue: request.dataType) else {
        return ToolResult(
            text: "Unknown type: \(request.dataType). Use list_health_types to see available types.",
            isError: true
        )
    }

    let samples = try await healthStore.querySamples(type: type, start: startDate, end: endDate)
    return ToolResult(text: samples.formatted())
}

// 3. Context injection: Tell agent what's available in system prompt
func buildSystemPrompt() -> String {
    let availableTypes = healthService.getAuthorizedTypes()

    return """
    ## Available Health Data

    You have access to these health metrics:
    \(availableTypes.map { "- \($0)" }.joined(separator: "\n"))

    Use read_health_data with any type above. For new types not listed,
    use list_health_types to discover what's available.
    """
}

Benefits:

• Agent can use any API capability, including ones added after your code shipped
• API is the validator, not your enum definition
• Smaller tool surface (2-3 tools vs N tools)
• Agent naturally discovers capabilities by asking
• Works with any API that has introspection (HealthKit, GraphQL, OpenAPI)

Every data type the agent can create, it should be able to read, update, and delete. Incomplete CRUD = broken action parity.

Anti-pattern: Create-only tools

// ❌ Can create but not modify or delete
tool("create_experiment", { hypothesis, variable, metric })
tool("write_journal_entry", { content, author, tags })
// User: "Delete that experiment" → Agent: "I can't do that"

Correct: Full CRUD for each entity

// ✅ Complete CRUD
tool("create_experiment", { hypothesis, variable, metric })
tool("read_experiment", { id })
tool("update_experiment", { id, updates: { hypothesis?, status?, endDate? } })
tool("delete_experiment", { id })

tool("create_journal_entry", { content, author, tags })
tool("read_journal", { query?, dateRange?, author? })
tool("update_journal_entry", { id, content, tags? })
tool("delete_journal_entry", { id })

The CRUD Audit: For each entity type in your app, verify:

• Create: Agent can create new instances
• Read: Agent can query/search/list instances
• Update: Agent can modify existing instances
• Delete: Agent can remove instances

If any operation is missing, users will eventually ask for it and the agent will fail.

Fundamentals:

• Tool names describe capability, not use case
• Inputs are data, not decisions
• Outputs are rich (enough for agent to verify)
• CRUD operations are separate tools (not one mega-tool)
• No business logic in tool implementations
• Error states clearly communicated via isError
• Descriptions explain what the tool does, not when to use it

Dynamic Capability Discovery (for agent-native apps):

• For external APIs where agent should have full access, use dynamic discovery
• Include a list_* or discover_* tool for each API surface
• Use string inputs (not enums) when the API validates
• Inject available capabilities into system prompt at runtime
• Only use static tool mapping if intentionally limiting agent scope

CRUD Completeness:

• Every entity has create, read, update, delete operations
• Every UI action has a corresponding agent tool
• Test: "Can the agent undo what it just did?"