Tools | xmcp

Tools are functions that your LLM can actively call and decides when to use based on user requests. They enable AI models to perform actions such as writing to databases, calling external APIs, modifying files, or triggering other logic.

For the complete documentation index, see llms.txt. Markdown variants of every page are available by appending .md to the URL.

By default, xmcp detects files under the /src/tools/ directory and registers them as tools, but you can specify a custom directory if you prefer. The directory to use can be configured in the xmcp.config.ts file.

A tool file consists of three main exports:

Default: The tool handler function.
Schema (optional): The input parameters using Zod schemas.
Metadata (optional): The tool's identity and behavior hints. If omitted, the name is inferred from the file name and the description defaults to a placeholder.

src/tools/greet.ts

import { z } from "zod";
import { type InferSchema } from "xmcp";

// Define the schema for tool parameters
export const schema = {
  name: z.string().describe("The name of the user to greet"),
};

// Define tool metadata
export const metadata = {
  name: "greet",
  description: "Greet the user",
  annotations: {
    title: "Greet the user",
    readOnlyHint: true,
    destructiveHint: false,
    idempotentHint: true,
  },
};

// Tool implementation
export default async function greet({ name }: InferSchema<typeof schema>) {
  return `Hello, ${name}!`;
}

If you're returning a string or number only, you can shortcut the return value to be the string or number directly.

Code

export default async function greet({ name }: InferSchema<typeof schema>) {
  return `Hello, ${name}!`;
}

Schema Definition

The schema defines your tool's input parameters using Zod. Use .describe() on each parameter to help LLMs understand how to use your tool correctly.

src/tools/create-user.ts

import { z } from "zod";
import { type InferSchema } from "xmcp";

export const schema = {
  name: z.string().describe("User's full name"),
  email: z.string().email().describe("Valid email address"),
  age: z.number().min(18).optional().describe("User's age (18+)"),
  role: z.enum(["admin", "user"]).describe("User role"),
};

export default async function createUser(args: InferSchema<typeof schema>) {
  // args is automatically typed: { name: string; email: string; age?: number; role: "admin" | "user" }
  const { name, email, age, role } = args;
  // Implementation here
}

Type Inference

The InferSchema utility automatically infers TypeScript types from your Zod schema, giving you full type safety without manual type definitions:

Code

import { type InferSchema } from "xmcp";

export const schema = {
  tags: z.array(z.string()).describe("List of tags"),
  metadata: z
    .object({
      priority: z.number(),
      assignee: z.string().optional(),
    })
    .describe("Task metadata"),
};

// TypeScript infers:
// {
//   tags: string[];
//   metadata: { priority: number; assignee?: string };
// }
export default async function handler(args: InferSchema<typeof schema>) {
  // Full autocomplete and type checking
  args.tags.forEach((tag) => console.log(tag));
  args.metadata.priority; // number
  args.metadata.assignee; // string | undefined
}

Metadata

The metadata export defines your tool's identity and provides behavioral hints to LLMs and clients.

src/tools/delete-user.ts

import { type ToolMetadata } from "xmcp";

export const metadata: ToolMetadata = {
  name: "delete-user",
  description: "Permanently delete a user account",
  annotations: {
    title: "Delete User Account",
    destructiveHint: true,
    idempotentHint: false,
  },
};

Core Properties

name (required)

Unique identifier for the tool
Defaults to the filename if not provided
Use kebab-case (e.g., get-user-profile)

description (required)

Clear explanation of what the tool does
Defaults to placeholder if not provided
Critical for LLM tool discovery and selection

Annotations

Behavioral hints that help LLMs and UIs understand how to use your tool:

Code

annotations: {
  // Human-readable title displayed in UIs
  title: "Create New Task",

  // Tool doesn't modify its environment (safe to retry)
  readOnlyHint: true,

  // Tool may perform destructive updates (use with caution)
  destructiveHint: false,

  // Repeated calls with same args have no additional effect
  idempotentHint: true,

  // Tool interacts with external entities (APIs, databases)
  openWorldHint: true,
}

MCP Apps metadata

Code

export const metadata: ToolMetadata = {
  name: "show-analytics",
  description: "Display analytics dashboard",
  _meta: {
    ui: {
      csp: {
        connectDomains: ["https://api.analytics.com"],
        resourceDomains: ["https://cdn.analytics.com"],
      },
      domain: "https://analytics-widget.example.com",
      prefersBorder: true,
    },
  },
};

Resource-specific properties:

csp.connectDomains - Origins for fetch/XHR/WebSocket connections
csp.resourceDomains - Origins for images, scripts, stylesheets, fonts, media
domain - Optional dedicated subdomain for the widget's sandbox origin
prefersBorder - Request visible border + background (true/false/omitted)

Handler Types

Tools support three types of handlers, each suited for different use cases:

Type	Best For	Returns
Standard	Data queries, calculations, API calls	Unstructured or structured content
Template Literal	Simple widgets with external scripts	HTML string
React Component	Interactive, stateful widgets	React component

1. Standard Handlers

Standard handlers are functions that return text, structured content, or simple data. This is the default approach for most tools.

When to use:

Performing calculations or data transformations
Calling external APIs and returning results
Querying databases
Any task that returns text or structured data without UI interaction

src/tools/calculate.ts

import { z } from "zod";
import { type InferSchema } from "xmcp";

export const schema = {
  operation: z.enum(["add", "subtract"]),
  a: z.number(),
  b: z.number(),
};

export const metadata = {
  name: "calculate",
  description: "Perform basic calculations",
};

export default async function calculate({
  operation,
  a,
  b,
}: InferSchema<typeof schema>) {
  const result = operation === "add" ? a + b : a - b;
  return `Result: ${result}`;
}

Elicitation

Tool handlers also receive an extra argument. Use extra.elicit() when you want the client to collect a small piece of user input before the tool continues.

When the client sends an MCP initialize request, extra.clientInfo is available with protocol-level client identity (name, version, and optional fields like title). In stdio, xmcp keeps that identity after initialization for the lifetime of the connection.

HTTP transports are strictly stateless. Tool calls only receive extra.clientInfo when the current request includes client identity. For post-initialize tool calls, repeat the identity with request headers:

Code

x-mcp-client-name: cursor
x-mcp-client-version: 0.50.1
x-mcp-client-title: Cursor

src/tools/preview-elicitation.ts

import { type ToolExtraArguments, type ToolMetadata } from "xmcp";

export const metadata: ToolMetadata = {
  name: "preview-elicitation",
  description: "Preview a basic extra.elicit() flow in MCPJam",
  annotations: {
    title: "Preview elicitation",
    readOnlyHint: true,
    destructiveHint: false,
    idempotentHint: true,
  },
};

export default async function previewElicitation(
  _: any,
  extra: ToolExtraArguments
) {
  const result = await extra.elicit({
    message: "Choose a deployment target",
    requestedSchema: {
      type: "object",
      properties: {
        environment: {
          type: "string",
          title: "Environment",
          enum: ["staging", "production"],
          enumNames: ["Staging", "Production"],
          default: "staging",
        },
      },
      required: ["environment"],
    },
  });

  return JSON.stringify(result, null, 2);
}

Quick check with MCPJam

From the repo root, run pnpm --dir examples/http-transport dev.
In another terminal, run npx @mcpjam/inspector@latest.
Connect MCPJam to http://127.0.0.1:3001/mcp.
Call preview-elicitation.
MCPJam opens a small form with an environment select. Accepting returns action: "accept" plus content.environment. Cancel or decline returns the matching action.

Multi round-trip input (`inputRequired`)

Protocol revision 2026-07-28 replaces server-initiated requests with multi round-trip requests: the tool returns an input_required result describing what it needs, the client collects it, and retries the same tool call with inputResponses attached. xmcp re-exports the SDK helpers, so a tool that needs user input before continuing looks like this:

src/tools/preview-input-required.ts

import { z } from "zod";
import {
  acceptedContent,
  inputRequired,
  type InferSchema,
  type ToolExtraArguments,
} from "xmcp";

export const schema = {
  theme: z.string().describe("The theme to apply"),
};

export const metadata = {
  name: "preview-input-required",
  description: "Ask the user to confirm before applying a theme",
};

export default async function previewInputRequired(
  { theme }: InferSchema<typeof schema>,
  extra: ToolExtraArguments
) {
  const answer = acceptedContent<{ confirmed: boolean }>(
    extra.inputResponses,
    "confirmation"
  );

  if (!answer) {
    return inputRequired({
      inputRequests: {
        confirmation: inputRequired.elicit({
          message: `Apply the "${theme}" theme?`,
          requestedSchema: {
            type: "object",
            properties: {
              confirmed: { type: "boolean", title: "Confirm" },
            },
            required: ["confirmed"],
          },
        }),
      },
    });
  }

  return answer.confirmed
    ? `Theme "${theme}" applied.`
    : `Theme change cancelled.`;
}

The handler runs once per round: the first call returns the embedded elicitation, the retry finds the answer in extra.inputResponses and completes. On 2025-era connections the SDK's legacy shim converts the inputRequired return into a real elicitation request automatically, so the same tool serves both client generations. To carry server state across rounds (remember: it round-trips through the client), mint and verify it with createRequestStateCodec, also re-exported from xmcp.

Sampling

Use extra.sample() when a tool needs an LLM completion from the connected client. The client keeps control of model access, selection, and permissions, so the server needs no model API key. Sampling only works when the connected client advertises the sampling capability; other clients reject the request.

The request takes messages (text, image, or audio content), a required maxTokens, and optional systemPrompt, modelPreferences (model hints plus cost/speed/intelligence priorities), temperature, stopSequences, includeContext, and metadata. The result contains the model the client picked, the assistant content, and an optional stopReason.

src/tools/preview-sampling.ts

import { z } from "zod";
import {
  type InferSchema,
  type ToolExtraArguments,
  type ToolMetadata,
} from "xmcp";

export const schema = {
  text: z.string().describe("Text for the client's model to summarize"),
};

export const metadata: ToolMetadata = {
  name: "preview-sampling",
  description: "Preview a basic extra.sample() flow in MCPJam",
  annotations: {
    title: "Preview sampling",
    readOnlyHint: true,
    destructiveHint: false,
    idempotentHint: true,
  },
};

export default async function previewSampling(
  { text }: InferSchema<typeof schema>,
  extra: ToolExtraArguments
) {
  const result = await extra.sample({
    messages: [
      {
        role: "user",
        content: { type: "text", text: `Summarize in one sentence:\n${text}` },
      },
    ],
    systemPrompt: "You summarize text concisely.",
    modelPreferences: {
      speedPriority: 0.8,
    },
    maxTokens: 200,
  });

  return JSON.stringify(result, null, 2);
}

Quick check with MCPJam

From the repo root, run pnpm --dir examples/http-transport dev.
In another terminal, run npx @mcpjam/inspector@latest.
Connect MCPJam to http://127.0.0.1:3001/mcp.
Call preview-sampling with any text.
MCPJam shows the incoming sampling request for approval. Approving runs the completion with its configured model and the tool returns the model, content, and stopReason from the result.

2. Template Literal Handlers

Return HTML directly to create interactive widgets. xmcp automatically generates the widget resource.

src/tools/show-chart.ts

import { type ToolMetadata } from "xmcp";

export const metadata: ToolMetadata = {
  name: "show-chart",
  description: "Display an interactive chart",
  _meta: {
    ui: {
      csp: {
        resourceDomains: ["https://cdn.jsdelivr.net"],
      },
    },
  },
};

export default async function showChart() {
  return `
    <div id="chart-container">
      <h2>Sales Data</h2>
      <canvas id="chart"></canvas>
    </div>
    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
    <script>
      // Chart initialization code
    </script>
  `;
}

3. React Component Handlers

Return React components for interactive, composable widgets. xmcp renders the component to HTML and generates a widget resource automatically.

src/tools/interactive-todo.tsx

import { type ToolMetadata } from "xmcp";
import { useState } from "react";

export const metadata: ToolMetadata = {
  name: "interactive-todo",
  description: "Interactive todo list widget",
  _meta: {
    ui: {
      prefersBorder: true,
    },
  },
};

export default function InteractiveTodo() {
  const [todos, setTodos] = useState<string[]>([]);
  const [input, setInput] = useState("");

  const addTodo = () => {
    if (input.trim()) {
      setTodos([...todos, input]);
      setInput("");
    }
  };

  return (
    <div>
      <h2>Todo List</h2>
      <input
        type="text"
        value={input}
        onChange={(e) => setInput(e.target.value)}
        placeholder="Add a todo..."
      />
      <button onClick={addTodo}>Add</button>
      <ul>
        {todos.map((todo, idx) => (
          <li key={idx}>{todo}</li>
        ))}
      </ul>
    </div>
  );
}

Setup Requirements:

Use .tsx file extension for React component tools
Install React dependencies: npm install react react-dom
Configure tsconfig.json:

Code

{
  "compilerOptions": {
    "jsx": "react-jsx"
  }
}

Return Values

Tools support multiple return formats depending on your needs:

Simple Values

Return strings or numbers directly - xmcp automatically wraps them in the proper format:

Code

export default async function calculate() {
  return "Result: 42"; // or return 42;
}

Content Array

Return an object with a content array for rich media responses:

Code

export default async function getProfile() {
  return {
    content: [
      {
        type: "text",
        text: "Profile information:",
      },
      {
        type: "image",
        data: "base64encodeddata",
        mimeType: "image/jpeg",
      },
      {
        type: "resource_link",
        name: "Full Profile",
        uri: "resource://profile/john",
      },
    ],
  };
}

Supported content types:

text - Plain text content
image - Base64-encoded images with mimeType
audio - Base64-encoded audio with mimeType
resource_link - Links to MCP resources

Structured Outputs

You can declare an outputSchema when your tool returns structuredContent to enforce validation:

Code

import { z } from "zod";

export const outputSchema = {
  user: z.object({
    id: z.number(),
    name: z.string(),
  }),
};

Return structured data using the structuredContent property:

Code

export default async function getUserData() {
  return {
    structuredContent: {
      user: {
        id: 123,
        name: "John Doe",
      },
    },
  };
}

structuredContent works without declaring outputSchema. If outputSchema is declared and structuredContent is returned, structuredContent must conform to it. If your handler returns a primitive (string or number) and outputSchema has exactly one field that accepts it, xmcp auto-injects it into structuredContent using that field. Undeclared keys are rejected when validating structuredContent against outputSchema. You can also return a plain object directly (for example return content) and xmcp will treat it as structuredContent when outputSchema is declared. When structuredContent is returned without content, xmcp auto-generates a text fallback (JSON.stringify(structuredContent)) for compatibility with clients that only render content.

Combined Response

Return both content and structuredContent for backwards compatibility. If the client cannot process structured outputs, it will fallback to content.

Code

export default async function getData() {
  return {
    content: [
      {
        type: "text",
        text: "Data retrieved successfully",
      },
    ],
    structuredContent: {
      data: { key: "value" },
    },
  };
}

Troubleshooting

Tool Loading Errors

When xmcp starts, it loads every file under your tools directory.

Empty tool files are skipped with a friendly warning
Files without a default export are skipped with a friendly warning
Real syntax or import errors still fail normally so you can see the full stack trace

For example, if src/tools/draft.ts is empty, startup will log:

Code

[xmcp] Failed to load tool file: src/tools/draft.ts
   -> File is empty.
[xmcp] 1 tool skipped due to empty files or missing default exports

If the file exists but does not export a default handler, startup will log:

Code

[xmcp] Failed to load tool file: src/tools/draft.ts
   -> File does not export a default tool handler.

CLI Scaffolding

You can use the CLI to scaffold tools, resources, and prompts.

Create a tool

Code

xmcp create tool my-tool

Create a resource

Code

xmcp create resource my-resource

Create a prompt

Code

xmcp create prompt my-prompt

Output

Each command creates a starter file in the default directory for that primitive:

xmcp create tool my-tool → src/tools/my-tool.ts
xmcp create resource my-resource → src/resources/my-resource.ts
xmcp create prompt my-prompt → src/prompts/my-prompt.ts

The generated file already includes the basic exports you need to continue:

tools: schema, metadata, and a default function
resources: metadata and a default function
prompts: schema, metadata, and a default function

So instead of starting from an empty file, you get a ready-to-edit template with placeholder descriptions and example return values.