Guides

Custom Tools

Build and integrate custom tools to extend Claude Agent SDK functionality

Custom tools allow you to extend Claude Code's capabilities with your own functionality through in-process MCP servers, enabling Claude to interact with external services, APIs, or perform specialized operations.

Creating Custom Tools

Use the createSdkMcpServer and tool helper functions to define type-safe custom tools:

TypeScript

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

// Create an SDK MCP server with custom tools
const customServer = createSdkMcpServer({
  name: "my-custom-tools",
  version: "1.0.0",
  tools: [
    tool(
      "get_weather",
      "Get current temperature for a location using coordinates",
      {
        latitude: z.number().describe("Latitude coordinate"),
        longitude: z.number().describe("Longitude coordinate")
      },
      async (args) => {
        const response = await fetch(`https://api.open-meteo.com/v1/forecast?latitude=${args.latitude}&longitude=${args.longitude}&current=temperature_2m&temperature_unit=fahrenheit`);
        const data = await response.json();

        return {
          content: [{
            type: "text",
            text: `Temperature: ${data.current.temperature_2m}°F`
          }]
        };
      }
    )
  ]
});

Python

from claude_agent_sdk import tool, create_sdk_mcp_server, ClaudeSDKClient, ClaudeAgentOptions
from typing import Any
import aiohttp

# Define a custom tool using the @tool decorator
@tool("get_weather", "Get current temperature for a location using coordinates", {"latitude": float, "longitude": float})
async def get_weather(args: dict[str, Any]) -> dict[str, Any]:
    # Call weather API
    async with aiohttp.ClientSession() as session:
        async with session.get(
            f"https://api.open-meteo.com/v1/forecast?latitude={args['latitude']}&longitude={args['longitude']}&current=temperature_2m&temperature_unit=fahrenheit"
        ) as response:
            data = await response.json()

    return {
        "content": [{
            "type": "text",
            "text": f"Temperature: {data['current']['temperature_2m']}°F"
        }]
    }

# Create an SDK MCP server with the custom tool
custom_server = create_sdk_mcp_server(
    name="my-custom-tools",
    version="1.0.0",
    tools=[get_weather]  # Pass the decorated function
)

Using Custom Tools

Pass the custom server to the query function via the mcpServers option as a dictionary/object.

Important: Custom MCP tools require streaming input mode. You must use an async generator/iterable for the prompt parameter - a simple string will not work with MCP servers.

Tool Name Format

When MCP tools are exposed to Claude, their names follow a specific format:

Pattern: mcp__{server_name}__{tool_name}
Example: A tool named get_weather in server my-custom-tools becomes mcp__my-custom-tools__get_weather

Configuring Allowed Tools

You can control which tools Claude can use via the allowedTools option:

import { query } from "@anthropic-ai/claude-agent-sdk";

// Use the custom tools in your query with streaming input
async function* generateMessages() {
  yield {
    type: "user" as const,
    message: {
      role: "user" as const,
      content: "What's the weather in San Francisco?"
    }
  };
}

for await (const message of query({
  prompt: generateMessages(),  // Use async generator for streaming input
  options: {
    mcpServers: {
      "my-custom-tools": customServer  // Pass as object/dictionary, not array
    },
    // Optionally specify which tools Claude can use
    allowedTools: [
      "mcp__my-custom-tools__get_weather",  // Allow the weather tool
      // Add other tools as needed
    ],
    maxTurns: 3
  }
})) {
  if (message.type === "result" && message.subtype === "success") {
    console.log(message.result);
  }
}

Multiple Tools Example

When your MCP server has multiple tools, you can selectively allow them:

const multiToolServer = createSdkMcpServer({
  name: "utilities",
  version: "1.0.0",
  tools: [
    tool("calculate", "Perform calculations", { /* ... */ }, async (args) => { /* ... */ }),
    tool("translate", "Translate text", { /* ... */ }, async (args) => { /* ... */ }),
    tool("search_web", "Search the web", { /* ... */ }, async (args) => { /* ... */ })
  ]
});

// Allow only specific tools with streaming input
async function* generateMessages() {
  yield {
    type: "user" as const,
    message: {
      role: "user" as const,
      content: "Calculate 5 + 3 and translate 'hello' to Spanish"
    }
  };
}

for await (const message of query({
  prompt: generateMessages(),  // Use async generator for streaming input
  options: {
    mcpServers: {
      utilities: multiToolServer
    },
    allowedTools: [
      "mcp__utilities__calculate",   // Allow calculator
      "mcp__utilities__translate",   // Allow translator
      // "mcp__utilities__search_web" is NOT allowed
    ]
  }
})) {
  // Process messages
}

Type Safety with Python

The @tool decorator supports various schema definition approaches for type safety:

import { z } from "zod";

tool(
  "process_data",
  "Process structured data with type safety",
  {
    // Zod schema defines both runtime validation and TypeScript types
    data: z.object({
      name: z.string(),
      age: z.number().min(0).max(150),
      email: z.string().email(),
      preferences: z.array(z.string()).optional()
    }),
    format: z.enum(["json", "csv", "xml"]).default("json")
  },
  async (args) => {
    // args is fully typed based on the schema
    // TypeScript knows: args.data.name is string, args.data.age is number, etc.
    console.log(`Processing ${args.data.name}'s data as ${args.format}`);
    
    // Your processing logic here
    return {
      content: [{
        type: "text",
        text: `Processed data for ${args.data.name}`
      }]
    };
  }
)

Error Handling

Handle errors gracefully to provide meaningful feedback:

tool(
  "fetch_data",
  "Fetch data from an API",
  {
    endpoint: z.string().url().describe("API endpoint URL")
  },
  async (args) => {
    try {
      const response = await fetch(args.endpoint);
      
      if (!response.ok) {
        return {
          content: [{
            type: "text",
            text: `API error: ${response.status} ${response.statusText}`
          }]
        };
      }
      
      const data = await response.json();
      return {
        content: [{
          type: "text",
          text: JSON.stringify(data, null, 2)
        }]
      };
    } catch (error) {
      return {
        content: [{
          type: "text",
          text: `Failed to fetch data: ${error.message}`
        }]
      };
    }
  }
)

Example Tools

Database Query Tool

const databaseServer = createSdkMcpServer({
  name: "database-tools",
  version: "1.0.0",
  tools: [
    tool(
      "query_database",
      "Execute a database query",
      {
        query: z.string().describe("SQL query to execute"),
        params: z.array(z.any()).optional().describe("Query parameters")
      },
      async (args) => {
        const results = await db.query(args.query, args.params || []);
        return {
          content: [{
            type: "text",
            text: `Found ${results.length} rows:\n${JSON.stringify(results, null, 2)}`
          }]
        };
      }
    )
  ]
});

API Gateway Tool

const apiGatewayServer = createSdkMcpServer({
  name: "api-gateway",
  version: "1.0.0",
  tools: [
    tool(
      "api_request",
      "Make authenticated API requests to external services",
      {
        service: z.enum(["stripe", "github", "openai", "slack"]).describe("Service to call"),
        endpoint: z.string().describe("API endpoint path"),
        method: z.enum(["GET", "POST", "PUT", "DELETE"]).describe("HTTP method"),
        body: z.record(z.any()).optional().describe("Request body"),
        query: z.record(z.string()).optional().describe("Query parameters")
      },
      async (args) => {
        const config = {
          stripe: { baseUrl: "https://api.stripe.com/v1", key: process.env.STRIPE_KEY },
          github: { baseUrl: "https://api.github.com", key: process.env.GITHUB_TOKEN },
          openai: { baseUrl: "https://api.openai.com/v1", key: process.env.OPENAI_KEY },
          slack: { baseUrl: "https://slack.com/api", key: process.env.SLACK_TOKEN }
        };
        
        const { baseUrl, key } = config[args.service];
        const url = new URL(`${baseUrl}${args.endpoint}`);
        
        if (args.query) {
          Object.entries(args.query).forEach(([k, v]) => url.searchParams.set(k, v));
        }
        
        const response = await fetch(url, {
          method: args.method,
          headers: { Authorization: `Bearer ${key}`, "Content-Type": "application/json" },
          body: args.body ? JSON.stringify(args.body) : undefined
        });
        
        const data = await response.json();
        return {
          content: [{
            type: "text",
            text: JSON.stringify(data, null, 2)
          }]
        };
      }
    )
  ]
});

Calculator Tool

const calculatorServer = createSdkMcpServer({
  name: "calculator",
  version: "1.0.0",
  tools: [
    tool(
      "calculate",
      "Perform mathematical calculations",
      {
        expression: z.string().describe("Mathematical expression to evaluate"),
        precision: z.number().optional().default(2).describe("Decimal precision")
      },
      async (args) => {
        try {
          // Use a safe math evaluation library in production
          const result = eval(args.expression); // Example only!
          const formatted = Number(result).toFixed(args.precision);
          
          return {
            content: [{
              type: "text",
              text: `${args.expression} = ${formatted}`
            }]
          };
        } catch (error) {
          return {
            content: [{
              type: "text",
              text: `Error: Invalid expression - ${error.message}`
            }]
          };
        }
      }
    ),
    tool(
      "compound_interest",
      "Calculate compound interest for an investment",
      {
        principal: z.number().positive().describe("Initial investment amount"),
        rate: z.number().describe("Annual interest rate (as decimal, e.g., 0.05 for 5%)"),
        time: z.number().positive().describe("Investment period in years"),
        n: z.number().positive().default(12).describe("Compounding frequency per year")
      },
      async (args) => {
        const amount = args.principal * Math.pow(1 + args.rate / args.n, args.n * args.time);
        const interest = amount - args.principal;
        
        return {
          content: [{
            type: "text",
            text: `Investment Analysis:\n` +
                  `Principal: $${args.principal.toFixed(2)}\n` +
                  `Rate: ${(args.rate * 100).toFixed(2)}%\n` +
                  `Time: ${args.time} years\n` +
                  `Compounding: ${args.n} times per year\n\n` +
                  `Final Amount: $${amount.toFixed(2)}\n` +
                  `Interest Earned: $${interest.toFixed(2)}\n` +
                  `Return: ${((interest / args.principal) * 100).toFixed(2)}%`
          }]
        };
      }
    )
  ]
});

Custom Tools

Build and integrate custom tools to extend Claude Agent SDK functionality

Creating Custom Tools

Use the createSdkMcpServer and tool helper functions to define type-safe custom tools:

TypeScript

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

// Create an SDK MCP server with custom tools
const customServer = createSdkMcpServer({
  name: "my-custom-tools",
  version: "1.0.0",
  tools: [
    tool(
      "get_weather",
      "Get current temperature for a location using coordinates",
      {
        latitude: z.number().describe("Latitude coordinate"),
        longitude: z.number().describe("Longitude coordinate")
      },
      async (args) => {
        const response = await fetch(`https://api.open-meteo.com/v1/forecast?latitude=${args.latitude}&longitude=${args.longitude}&current=temperature_2m&temperature_unit=fahrenheit`);
        const data = await response.json();

        return {
          content: [{
            type: "text",
            text: `Temperature: ${data.current.temperature_2m}°F`
          }]
        };
      }
    )
  ]
});

Python

from claude_agent_sdk import tool, create_sdk_mcp_server, ClaudeSDKClient, ClaudeAgentOptions
from typing import Any
import aiohttp

# Define a custom tool using the @tool decorator
@tool("get_weather", "Get current temperature for a location using coordinates", {"latitude": float, "longitude": float})
async def get_weather(args: dict[str, Any]) -> dict[str, Any]:
    # Call weather API
    async with aiohttp.ClientSession() as session:
        async with session.get(
            f"https://api.open-meteo.com/v1/forecast?latitude={args['latitude']}&longitude={args['longitude']}&current=temperature_2m&temperature_unit=fahrenheit"
        ) as response:
            data = await response.json()

    return {
        "content": [{
            "type": "text",
            "text": f"Temperature: {data['current']['temperature_2m']}°F"
        }]
    }

# Create an SDK MCP server with the custom tool
custom_server = create_sdk_mcp_server(
    name="my-custom-tools",
    version="1.0.0",
    tools=[get_weather]  # Pass the decorated function
)

Using Custom Tools

Pass the custom server to the query function via the mcpServers option as a dictionary/object.

Important: Custom MCP tools require streaming input mode. You must use an async generator/iterable for the prompt parameter - a simple string will not work with MCP servers.

Tool Name Format

When MCP tools are exposed to Claude, their names follow a specific format:

Pattern: mcp__{server_name}__{tool_name}
Example: A tool named get_weather in server my-custom-tools becomes mcp__my-custom-tools__get_weather

Configuring Allowed Tools

You can control which tools Claude can use via the allowedTools option:

import { query } from "@anthropic-ai/claude-agent-sdk";

// Use the custom tools in your query with streaming input
async function* generateMessages() {
  yield {
    type: "user" as const,
    message: {
      role: "user" as const,
      content: "What's the weather in San Francisco?"
    }
  };
}

for await (const message of query({
  prompt: generateMessages(),  // Use async generator for streaming input
  options: {
    mcpServers: {
      "my-custom-tools": customServer  // Pass as object/dictionary, not array
    },
    // Optionally specify which tools Claude can use
    allowedTools: [
      "mcp__my-custom-tools__get_weather",  // Allow the weather tool
      // Add other tools as needed
    ],
    maxTurns: 3
  }
})) {
  if (message.type === "result" && message.subtype === "success") {
    console.log(message.result);
  }
}

Multiple Tools Example

When your MCP server has multiple tools, you can selectively allow them:

const multiToolServer = createSdkMcpServer({
  name: "utilities",
  version: "1.0.0",
  tools: [
    tool("calculate", "Perform calculations", { /* ... */ }, async (args) => { /* ... */ }),
    tool("translate", "Translate text", { /* ... */ }, async (args) => { /* ... */ }),
    tool("search_web", "Search the web", { /* ... */ }, async (args) => { /* ... */ })
  ]
});

// Allow only specific tools with streaming input
async function* generateMessages() {
  yield {
    type: "user" as const,
    message: {
      role: "user" as const,
      content: "Calculate 5 + 3 and translate 'hello' to Spanish"
    }
  };
}

for await (const message of query({
  prompt: generateMessages(),  // Use async generator for streaming input
  options: {
    mcpServers: {
      utilities: multiToolServer
    },
    allowedTools: [
      "mcp__utilities__calculate",   // Allow calculator
      "mcp__utilities__translate",   // Allow translator
      // "mcp__utilities__search_web" is NOT allowed
    ]
  }
})) {
  // Process messages
}

Type Safety with Python

The @tool decorator supports various schema definition approaches for type safety:

import { z } from "zod";

tool(
  "process_data",
  "Process structured data with type safety",
  {
    // Zod schema defines both runtime validation and TypeScript types
    data: z.object({
      name: z.string(),
      age: z.number().min(0).max(150),
      email: z.string().email(),
      preferences: z.array(z.string()).optional()
    }),
    format: z.enum(["json", "csv", "xml"]).default("json")
  },
  async (args) => {
    // args is fully typed based on the schema
    // TypeScript knows: args.data.name is string, args.data.age is number, etc.
    console.log(`Processing ${args.data.name}'s data as ${args.format}`);
    
    // Your processing logic here
    return {
      content: [{
        type: "text",
        text: `Processed data for ${args.data.name}`
      }]
    };
  }
)

Error Handling

Handle errors gracefully to provide meaningful feedback:

tool(
  "fetch_data",
  "Fetch data from an API",
  {
    endpoint: z.string().url().describe("API endpoint URL")
  },
  async (args) => {
    try {
      const response = await fetch(args.endpoint);
      
      if (!response.ok) {
        return {
          content: [{
            type: "text",
            text: `API error: ${response.status} ${response.statusText}`
          }]
        };
      }
      
      const data = await response.json();
      return {
        content: [{
          type: "text",
          text: JSON.stringify(data, null, 2)
        }]
      };
    } catch (error) {
      return {
        content: [{
          type: "text",
          text: `Failed to fetch data: ${error.message}`
        }]
      };
    }
  }
)

Example Tools

Database Query Tool

const databaseServer = createSdkMcpServer({
  name: "database-tools",
  version: "1.0.0",
  tools: [
    tool(
      "query_database",
      "Execute a database query",
      {
        query: z.string().describe("SQL query to execute"),
        params: z.array(z.any()).optional().describe("Query parameters")
      },
      async (args) => {
        const results = await db.query(args.query, args.params || []);
        return {
          content: [{
            type: "text",
            text: `Found ${results.length} rows:\n${JSON.stringify(results, null, 2)}`
          }]
        };
      }
    )
  ]
});

API Gateway Tool

const apiGatewayServer = createSdkMcpServer({
  name: "api-gateway",
  version: "1.0.0",
  tools: [
    tool(
      "api_request",
      "Make authenticated API requests to external services",
      {
        service: z.enum(["stripe", "github", "openai", "slack"]).describe("Service to call"),
        endpoint: z.string().describe("API endpoint path"),
        method: z.enum(["GET", "POST", "PUT", "DELETE"]).describe("HTTP method"),
        body: z.record(z.any()).optional().describe("Request body"),
        query: z.record(z.string()).optional().describe("Query parameters")
      },
      async (args) => {
        const config = {
          stripe: { baseUrl: "https://api.stripe.com/v1", key: process.env.STRIPE_KEY },
          github: { baseUrl: "https://api.github.com", key: process.env.GITHUB_TOKEN },
          openai: { baseUrl: "https://api.openai.com/v1", key: process.env.OPENAI_KEY },
          slack: { baseUrl: "https://slack.com/api", key: process.env.SLACK_TOKEN }
        };
        
        const { baseUrl, key } = config[args.service];
        const url = new URL(`${baseUrl}${args.endpoint}`);
        
        if (args.query) {
          Object.entries(args.query).forEach(([k, v]) => url.searchParams.set(k, v));
        }
        
        const response = await fetch(url, {
          method: args.method,
          headers: { Authorization: `Bearer ${key}`, "Content-Type": "application/json" },
          body: args.body ? JSON.stringify(args.body) : undefined
        });
        
        const data = await response.json();
        return {
          content: [{
            type: "text",
            text: JSON.stringify(data, null, 2)
          }]
        };
      }
    )
  ]
});

Calculator Tool

const calculatorServer = createSdkMcpServer({
  name: "calculator",
  version: "1.0.0",
  tools: [
    tool(
      "calculate",
      "Perform mathematical calculations",
      {
        expression: z.string().describe("Mathematical expression to evaluate"),
        precision: z.number().optional().default(2).describe("Decimal precision")
      },
      async (args) => {
        try {
          // Use a safe math evaluation library in production
          const result = eval(args.expression); // Example only!
          const formatted = Number(result).toFixed(args.precision);
          
          return {
            content: [{
              type: "text",
              text: `${args.expression} = ${formatted}`
            }]
          };
        } catch (error) {
          return {
            content: [{
              type: "text",
              text: `Error: Invalid expression - ${error.message}`
            }]
          };
        }
      }
    ),
    tool(
      "compound_interest",
      "Calculate compound interest for an investment",
      {
        principal: z.number().positive().describe("Initial investment amount"),
        rate: z.number().describe("Annual interest rate (as decimal, e.g., 0.05 for 5%)"),
        time: z.number().positive().describe("Investment period in years"),
        n: z.number().positive().default(12).describe("Compounding frequency per year")
      },
      async (args) => {
        const amount = args.principal * Math.pow(1 + args.rate / args.n, args.n * args.time);
        const interest = amount - args.principal;
        
        return {
          content: [{
            type: "text",
            text: `Investment Analysis:\n` +
                  `Principal: $${args.principal.toFixed(2)}\n` +
                  `Rate: ${(args.rate * 100).toFixed(2)}%\n` +
                  `Time: ${args.time} years\n` +
                  `Compounding: ${args.n} times per year\n\n` +
                  `Final Amount: $${amount.toFixed(2)}\n` +
                  `Interest Earned: $${interest.toFixed(2)}\n` +
                  `Return: ${((interest / args.principal) * 100).toFixed(2)}%`
          }]
        };
      }
    )
  ]
});

Creating Custom Tools

Using Custom Tools

Tool Name Format

Configuring Allowed Tools

Multiple Tools Example

Type Safety with Python

Error Handling

Example Tools

Database Query Tool

API Gateway Tool

Calculator Tool

Related Documentation

Creating Custom Tools

Using Custom Tools

Tool Name Format

Configuring Allowed Tools

Multiple Tools Example

Type Safety with Python

Error Handling

Example Tools

Database Query Tool

API Gateway Tool

Calculator Tool

Related Documentation