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Define custom tools with the Claude Agent SDK's in-process MCP server so Claude can call your functions, hit your APIs, and perform domain-specific operations.
Custom tools extend the Agent SDK by letting you define your own functions that Claude can call during a conversation. Using the SDK's in-process MCP server, you can give Claude access to databases, external APIs, domain-specific logic, or any other capability your application needs.
This guide covers how to define tools with input schemas and handlers, bundle them into an MCP server, pass them to query, and control which tools Claude can access. It also covers error handling, tool annotations, and returning non-text content like images.
| If you want to... | Do this |
|---|---|
| Define a tool | Use @tool (Python) or tool() (TypeScript) with a name, description, schema, and handler. See Create a custom tool. |
| Register a tool with Claude | Wrap in create_sdk_mcp_server / createSdkMcpServer and pass to mcpServers in query(). See Call a custom tool. |
| Pre-approve a tool | Add to your allowed tools. See Configure allowed tools. |
| Remove a built-in tool from Claude's context | Pass a tools array listing only the built-ins you want. See Configure allowed tools. |
| Let Claude call tools in parallel | Set readOnlyHint: true on tools with no side effects. See Add tool annotations. |
| Handle errors without stopping the loop | Return isError: true instead of throwing. See Handle errors. |
| Return images or files | Use image or resource blocks in the content array. See Return images and resources. |
| Scale to many tools | Use tool search to load tools on demand. |
A tool is defined by four parts, passed as arguments to the tool() helper in TypeScript or the @tool decorator in Python:
- Name: a unique identifier Claude uses to call the tool.
- Description: what the tool does. Claude reads this to decide when to call it.
- Input schema: the arguments Claude must provide. In TypeScript this is always a Zod schema, and the handler's
argsare typed from it automatically. In Python this is a dict mapping names to types, like{"latitude": float}, which the SDK converts to JSON Schema for you. The Python decorator also accepts a full JSON Schema dict directly when you need enums, ranges, optional fields, or nested objects. - Handler: the async function that runs when Claude calls the tool. It receives the validated arguments and must return an object with:
content(required): an array of result blocks, each with atypeof"text","image", or"resource". See Return images and resources for non-text blocks.isError(optional): set totrueto signal a tool failure so Claude can react to it. See Handle errors.
After defining a tool, wrap it in a server with createSdkMcpServer (TypeScript) or create_sdk_mcp_server (Python). The server runs in-process inside your application, not as a separate process.
This example defines a get_temperature tool and wraps it in an MCP server. It only sets up the tool; to pass it to query and run it, see Call a custom tool below.
@tool( "get_temperature", "Get the current temperature at a location", {"latitude": float, "longitude": float}, ) async def get_temperature(args: dict[str, Any]) -> dict[str, Any]: async with httpx.AsyncClient() as client: response = await client.get( "https://api.open-meteo.com/v1/forecast", params={ "latitude": args["latitude"], "longitude": args["longitude"], "current": "temperature_2m", "temperature_unit": "fahrenheit", }, ) data = response.json()
# Return a content array - Claude sees this as the tool result
return {
"content": [
{
"type": "text",
"text": f"Temperature: {data['current']['temperature_2m']}°F",
}
]
}
weather_server = create_sdk_mcp_server( name="weather", version="1.0.0", tools=[get_temperature], )
```typescript TypeScript theme={null}
import { tool, createSdkMcpServer } from "@anthropic-ai/claude-agent-sdk";
import { z } from "zod";
// Define a tool: name, description, input schema, handler
const getTemperature = tool(
"get_temperature",
"Get the current temperature at a location",
{
latitude: z.number().describe("Latitude coordinate"), // .describe() adds a field description Claude sees
longitude: z.number().describe("Longitude coordinate")
},
async (args) => {
// args is typed from the schema: { latitude: number; longitude: number }
const response = await fetch(
`https://api.open-meteo.com/v1/forecast?latitude=${args.latitude}&longitude=${args.longitude}¤t=temperature_2m&temperature_unit=fahrenheit`
);
const data: any = await response.json();
// Return a content array - Claude sees this as the tool result
return {
content: [{ type: "text", text: `Temperature: ${data.current.temperature_2m}°F` }]
};
}
);
// Wrap the tool in an in-process MCP server
const weatherServer = createSdkMcpServer({
name: "weather",
version: "1.0.0",
tools: [getTemperature]
});
See the tool() TypeScript reference or the @tool Python reference for full parameter details, including JSON Schema input formats and return value structure.
Pass the MCP server you created to query via the mcpServers option. The key in mcpServers becomes the {server_name} segment in each tool's fully qualified name: mcp__{server_name}__{tool_name}. List that name in allowedTools so the tool runs without a permission prompt.
These snippets reuse the weatherServer from the example above to ask Claude what the weather is in a specific location.
async def main(): options = ClaudeAgentOptions( mcp_servers={"weather": weather_server}, allowed_tools=["mcp__weather__get_temperature"], )
async for message in query(
prompt="What's the temperature in San Francisco?",
options=options,
):
# ResultMessage is the final message after all tool calls complete
if isinstance(message, ResultMessage) and message.subtype == "success":
print(message.result)
asyncio.run(main())
```typescript TypeScript theme={null}
import { query } from "@anthropic-ai/claude-agent-sdk";
for await (const message of query({
prompt: "What's the temperature in San Francisco?",
options: {
mcpServers: { weather: weatherServer },
allowedTools: ["mcp__weather__get_temperature"]
}
})) {
// "result" is the final message after all tool calls complete
if (message.type === "result" && message.subtype === "success") {
console.log(message.result);
}
}
A server holds as many tools as you list in its tools array. With more than one tool on a server, you can list each one in allowedTools individually or use the wildcard mcp__weather__* to cover every tool the server exposes.
The example below adds a second tool, get_precipitation_chance, to the weatherServer from the weather tool example and rebuilds it with both tools in the array.
return {
"content": [
{
"type": "text",
"text": f"Next {hours} hours: {'%, '.join(map(str, chances))}%",
}
]
}
weather_server = create_sdk_mcp_server( name="weather", version="1.0.0", tools=[get_temperature, get_precipitation_chance], )
```typescript TypeScript theme={null}
// Define a second tool for the same server
const getPrecipitationChance = tool(
"get_precipitation_chance",
"Get the hourly precipitation probability for a location",
{
latitude: z.number(),
longitude: z.number(),
hours: z
.number()
.int()
.min(1)
.max(24)
.default(12) // .default() makes the parameter optional
.describe("How many hours of forecast to return")
},
async (args) => {
const response = await fetch(
`https://api.open-meteo.com/v1/forecast?latitude=${args.latitude}&longitude=${args.longitude}&hourly=precipitation_probability&forecast_days=1`
);
const data: any = await response.json();
const chances = data.hourly.precipitation_probability.slice(0, args.hours);
return {
content: [{ type: "text", text: `Next ${args.hours} hours: ${chances.join("%, ")}%` }]
};
}
);
// Rebuild the server with both tools in the array
const weatherServer = createSdkMcpServer({
name: "weather",
version: "1.0.0",
tools: [getTemperature, getPrecipitationChance]
});
Every tool in this array consumes context window space on every turn. If you're defining dozens of tools, see tool search to load them on demand instead.
Tool annotations are optional metadata describing how a tool behaves. Pass them as the fifth argument to tool() helper in TypeScript or via the annotations keyword argument for the @tool decorator in Python. All hint fields are Booleans.
| Field | Default | Meaning |
|---|---|---|
readOnlyHint |
false |
Tool does not modify its environment. Controls whether the tool can be called in parallel with other read-only tools. |
destructiveHint |
true |
Tool may perform destructive updates. Informational only. |
idempotentHint |
false |
Repeated calls with the same arguments have no additional effect. Informational only. |
openWorldHint |
true |
Tool reaches systems outside your process. Informational only. |
Annotations are metadata, not enforcement. A tool marked readOnlyHint: true can still write to disk if that's what the handler does. Keep the annotation accurate to the handler.
This example adds readOnlyHint to the get_temperature tool from the weather tool example.
@tool( "get_temperature", "Get the current temperature at a location", {"latitude": float, "longitude": float}, annotations=ToolAnnotations( readOnlyHint=True ), # Lets Claude batch this with other read-only calls ) async def get_temperature(args): return {"content": [{"type": "text", "text": "..."}]}
```typescript TypeScript theme={null}
tool(
"get_temperature",
"Get the current temperature at a location",
{ latitude: z.number(), longitude: z.number() },
async (args) => ({ content: [{ type: "text", text: `...` }] }),
{ annotations: { readOnlyHint: true } } // Lets Claude batch this with other read-only calls
);
See ToolAnnotations in the TypeScript or Python reference.
The weather tool example registered a server and listed tools in allowedTools. This section covers how tool names are constructed and how to scope access when you have multiple tools or want to restrict built-ins.
When MCP tools are exposed to Claude, their names follow a specific format:
- Pattern:
mcp__{server_name}__{tool_name} - Example: A tool named
get_temperaturein serverweatherbecomesmcp__weather__get_temperature
The tools option and the allowed/disallowed lists operate on separate layers. tools controls which built-in tools appear in Claude's context. Allowed and disallowed tool lists control whether calls are approved or denied once Claude attempts them.
| Option | Layer | Effect |
|---|---|---|
tools: ["Read", "Grep"] |
Availability | Only the listed built-ins are in Claude's context. Unlisted built-ins are removed. MCP tools are unaffected. |
tools: [] |
Availability | All built-ins are removed. Claude can only use your MCP tools. |
| allowed tools | Permission | Listed tools run without a permission prompt. Unlisted tools remain available; calls go through the permission flow. |
| disallowed tools | Permission | Every call to a listed tool is denied. The tool stays in Claude's context, so Claude may still attempt it before the call is rejected. |
To limit which built-ins Claude can use, prefer tools over disallowed tools. Omitting a tool from tools removes it from context so Claude never attempts it; listing it in disallowedTools (Python: disallowed_tools) blocks the call but leaves the tool visible, so Claude may waste a turn trying it. See Configure permissions for the full evaluation order.
How your handler reports errors determines whether the agent loop continues or stops:
| What happens | Result |
|---|---|
| Handler throws an uncaught exception | Agent loop stops. Claude never sees the error, and the query call fails. |
Handler catches the error and returns isError: true (TS) / "is_error": True (Python) |
Agent loop continues. Claude sees the error as data and can retry, try a different tool, or explain the failure. |
The example below catches two kinds of failures inside the handler instead of letting them throw. A non-200 HTTP status is caught from the response and returned as an error result. A network error or invalid JSON is caught by the surrounding try/except (Python) or try/catch (TypeScript) and also returned as an error result. In both cases the handler returns normally and the agent loop continues.
@tool( "fetch_data", "Fetch data from an API", {"endpoint": str}, # Simple schema ) async def fetch_data(args: dict[str, Any]) -> dict[str, Any]: try: async with httpx.AsyncClient() as client: response = await client.get(args["endpoint"]) if response.status_code != 200: # Return the failure as a tool result so Claude can react to it. # is_error marks this as a failed call rather than odd-looking data. return { "content": [ { "type": "text", "text": f"API error: {response.status_code} {response.reason_phrase}", } ], "is_error": True, }
data = response.json()
return {"content": [{"type": "text", "text": json.dumps(data, indent=2)}]}
except Exception as e:
# Catching here keeps the agent loop alive. An uncaught exception
# would end the whole query() call.
return {
"content": [{"type": "text", "text": f"Failed to fetch data: {str(e)}"}],
"is_error": True,
}
```typescript TypeScript theme={null}
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 the failure as a tool result so Claude can react to it.
// isError marks this as a failed call rather than odd-looking data.
return {
content: [
{
type: "text",
text: `API error: ${response.status} ${response.statusText}`
}
],
isError: true
};
}
const data = await response.json();
return {
content: [
{
type: "text",
text: JSON.stringify(data, null, 2)
}
]
};
} catch (error) {
// Catching here keeps the agent loop alive. An uncaught throw
// would end the whole query() call.
return {
content: [
{
type: "text",
text: `Failed to fetch data: ${error instanceof Error ? error.message : String(error)}`
}
],
isError: true
};
}
}
);
The content array in a tool result accepts text, image, and resource blocks. You can mix them in the same response.
An image block carries the image bytes inline, encoded as base64. There is no URL field. To return an image that lives at a URL, fetch it in the handler, read the response bytes, and base64-encode them before returning. The result is processed as visual input.
| Field | Type | Notes |
|---|---|---|
type |
"image" |
|
data |
string |
Base64-encoded bytes. Raw base64 only, no data:image/...;base64, prefix |
mimeType |
string |
Required. For example image/png, image/jpeg, image/webp, image/gif |
@tool("fetch_image", "Fetch an image from a URL and return it to Claude", {"url": str}) async def fetch_image(args): async with httpx.AsyncClient() as client: # Fetch the image bytes response = await client.get(args["url"])
return {
"content": [
{
"type": "image",
"data": base64.b64encode(response.content).decode(
"ascii"
), # Base64-encode the raw bytes
"mimeType": response.headers.get(
"content-type", "image/png"
), # Read MIME type from the response
}
]
}
```typescript TypeScript theme={null}
tool(
"fetch_image",
"Fetch an image from a URL and return it to Claude",
{
url: z.string().url()
},
async (args) => {
const response = await fetch(args.url); // Fetch the image bytes
const buffer = Buffer.from(await response.arrayBuffer()); // Read into a Buffer for base64 encoding
const mimeType = response.headers.get("content-type") ?? "image/png";
return {
content: [
{
type: "image",
data: buffer.toString("base64"), // Base64-encode the raw bytes
mimeType
}
]
};
}
);
A resource block embeds a piece of content identified by a URI. The URI is a label for Claude to reference; the actual content rides in the block's text or blob field. Use this when your tool produces something that makes sense to address by name later, such as a generated file or a record from an external system.
| Field | Type | Notes |
|---|---|---|
type |
"resource" |
|
resource.uri |
string |
Identifier for the content. Any URI scheme |
resource.text |
string |
The content, if it's text. Provide this or blob, not both |
resource.blob |
string |
The content base64-encoded, if it's binary |
resource.mimeType |
string |
Optional |
This example shows a resource block returned from inside a tool handler. The URI file:///tmp/report.md is a label that Claude can reference later; the SDK does not read from that path.
return {
"content": [
{
"type": "resource",
"resource": {
"uri": "file:///tmp/report.md", # Label for Claude to reference, not a path the SDK reads
"mimeType": "text/markdown",
"text": "# Report\n...", # The actual content, inline
},
}
]
}These block shapes come from the MCP CallToolResult type. See the MCP specification for the full definition.
This tool converts values between units of length, temperature, and weight. A user can ask "convert 100 kilometers to miles" or "what is 72°F in Celsius," and Claude picks the right unit type and units from the request.
It demonstrates two patterns:
- Enum schemas:
unit_typeis constrained to a fixed set of values. In TypeScript, usez.enum(). In Python, the dict schema doesn't support enums, so the full JSON Schema dict is required. - Unsupported input handling: when a conversion pair isn't found, the handler returns
isError: trueso Claude can tell the user what went wrong rather than treating a failure as a normal result.
@tool( "convert_units", "Convert a value from one unit to another", { "type": "object", "properties": { "unit_type": { "type": "string", "enum": ["length", "temperature", "weight"], "description": "Category of unit", }, "from_unit": { "type": "string", "description": "Unit to convert from, e.g. kilometers, fahrenheit, pounds", }, "to_unit": {"type": "string", "description": "Unit to convert to"}, "value": {"type": "number", "description": "Value to convert"}, }, "required": ["unit_type", "from_unit", "to_unit", "value"], }, ) async def convert_units(args: dict[str, Any]) -> dict[str, Any]: conversions = { "length": { "kilometers_to_miles": lambda v: v * 0.621371, "miles_to_kilometers": lambda v: v * 1.60934, "meters_to_feet": lambda v: v * 3.28084, "feet_to_meters": lambda v: v * 0.3048, }, "temperature": { "celsius_to_fahrenheit": lambda v: (v * 9) / 5 + 32, "fahrenheit_to_celsius": lambda v: (v - 32) * 5 / 9, "celsius_to_kelvin": lambda v: v + 273.15, "kelvin_to_celsius": lambda v: v - 273.15, }, "weight": { "kilograms_to_pounds": lambda v: v * 2.20462, "pounds_to_kilograms": lambda v: v * 0.453592, "grams_to_ounces": lambda v: v * 0.035274, "ounces_to_grams": lambda v: v * 28.3495, }, }
key = f"{args['from_unit']}_to_{args['to_unit']}"
fn = conversions.get(args["unit_type"], {}).get(key)
if not fn:
return {
"content": [
{
"type": "text",
"text": f"Unsupported conversion: {args['from_unit']} to {args['to_unit']}",
}
],
"is_error": True,
}
result = fn(args["value"])
return {
"content": [
{
"type": "text",
"text": f"{args['value']} {args['from_unit']} = {result:.4f} {args['to_unit']}",
}
]
}
converter_server = create_sdk_mcp_server( name="converter", version="1.0.0", tools=[convert_units], )
```typescript TypeScript theme={null}
import { tool, createSdkMcpServer } from "@anthropic-ai/claude-agent-sdk";
import { z } from "zod";
const convert = tool(
"convert_units",
"Convert a value from one unit to another",
{
unit_type: z.enum(["length", "temperature", "weight"]).describe("Category of unit"),
from_unit: z
.string()
.describe("Unit to convert from, e.g. kilometers, fahrenheit, pounds"),
to_unit: z.string().describe("Unit to convert to"),
value: z.number().describe("Value to convert")
},
async (args) => {
type Conversions = Record<string, Record<string, (v: number) => number>>;
const conversions: Conversions = {
length: {
kilometers_to_miles: (v) => v * 0.621371,
miles_to_kilometers: (v) => v * 1.60934,
meters_to_feet: (v) => v * 3.28084,
feet_to_meters: (v) => v * 0.3048
},
temperature: {
celsius_to_fahrenheit: (v) => (v * 9) / 5 + 32,
fahrenheit_to_celsius: (v) => ((v - 32) * 5) / 9,
celsius_to_kelvin: (v) => v + 273.15,
kelvin_to_celsius: (v) => v - 273.15
},
weight: {
kilograms_to_pounds: (v) => v * 2.20462,
pounds_to_kilograms: (v) => v * 0.453592,
grams_to_ounces: (v) => v * 0.035274,
ounces_to_grams: (v) => v * 28.3495
}
};
const key = `${args.from_unit}_to_${args.to_unit}`;
const fn = conversions[args.unit_type]?.[key];
if (!fn) {
return {
content: [
{
type: "text",
text: `Unsupported conversion: ${args.from_unit} to ${args.to_unit}`
}
],
isError: true
};
}
const result = fn(args.value);
return {
content: [
{
type: "text",
text: `${args.value} ${args.from_unit} = ${result.toFixed(4)} ${args.to_unit}`
}
]
};
}
);
const converterServer = createSdkMcpServer({
name: "converter",
version: "1.0.0",
tools: [convert]
});
Once the server is defined, pass it to query the same way as the weather example. This example sends three different prompts in a loop to show the same tool handling different unit types. For each response, it inspects AssistantMessage objects (which contain the tool calls Claude made during that turn) and prints each ToolUseBlock before printing the final ResultMessage text. This lets you see when Claude is using the tool versus answering from its own knowledge.
async def main(): options = ClaudeAgentOptions( mcp_servers={"converter": converter_server}, allowed_tools=["mcp__converter__convert_units"], )
prompts = [
"Convert 100 kilometers to miles.",
"What is 72°F in Celsius?",
"How many pounds is 5 kilograms?",
]
for prompt in prompts:
async for message in query(prompt=prompt, options=options):
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, ToolUseBlock):
print(f"[tool call] {block.name}({block.input})")
elif isinstance(message, ResultMessage) and message.subtype == "success":
print(f"Q: {prompt}\nA: {message.result}\n")
asyncio.run(main())
```typescript TypeScript theme={null}
import { query } from "@anthropic-ai/claude-agent-sdk";
const prompts = [
"Convert 100 kilometers to miles.",
"What is 72°F in Celsius?",
"How many pounds is 5 kilograms?"
];
for (const prompt of prompts) {
for await (const message of query({
prompt,
options: {
mcpServers: { converter: converterServer },
allowedTools: ["mcp__converter__convert_units"]
}
})) {
if (message.type === "assistant") {
for (const block of message.message.content) {
if (block.type === "tool_use") {
console.log(`[tool call] ${block.name}`, block.input);
}
}
} else if (message.type === "result" && message.subtype === "success") {
console.log(`Q: ${prompt}\nA: ${message.result}\n`);
}
}
}
Custom tools wrap async functions in a standard interface. You can mix the patterns on this page in the same server: a single server can hold a database tool, an API gateway tool, and an image renderer alongside each other.
From here:
- If your server grows to dozens of tools, see tool search to defer loading them until Claude needs them.
- To connect to external MCP servers (filesystem, GitHub, Slack) instead of building your own, see Connect MCP servers.
- To control which tools run automatically versus requiring approval, see Configure permissions.