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docs: add mcp and skill session config (#106)
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NathanFlurry
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docs/custom-tools.mdx
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245
docs/custom-tools.mdx
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---
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title: "Custom Tools"
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description: "Give agents custom tools inside the sandbox using MCP servers or skills."
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sidebarTitle: "Custom Tools"
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icon: "wrench"
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---
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There are two ways to give agents custom tools that run inside the sandbox:
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| | MCP Server | Skill |
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|---|---|---|
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| **How it works** | Sandbox Agent spawns your MCP server process and routes tool calls to it via stdio | A markdown file that instructs the agent to run your script with `node` (or any command) |
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| **Tool discovery** | Agent sees tools automatically via MCP protocol | Agent reads instructions from the skill file |
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| **Best for** | Structured tools with typed inputs/outputs | Lightweight scripts with natural-language instructions |
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| **Requires** | `@modelcontextprotocol/sdk` dependency | Just a markdown file and a script |
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Both approaches execute code inside the sandbox, so your tools have full access to the sandbox filesystem, network, and installed system tools.
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## Option A: Tools via MCP
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<Steps>
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<Step title="Write your MCP server">
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Create an MCP server that exposes tools using `@modelcontextprotocol/sdk` with `StdioServerTransport`. This server will run inside the sandbox.
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```ts src/mcp-server.ts
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import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
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import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
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import { z } from "zod";
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const server = new McpServer({
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name: "rand",
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version: "1.0.0",
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});
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server.tool(
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"random_number",
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"Generate a random integer between min and max (inclusive)",
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{
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min: z.number().describe("Minimum value"),
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max: z.number().describe("Maximum value"),
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},
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async ({ min, max }) => ({
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content: [{ type: "text", text: String(Math.floor(Math.random() * (max - min + 1)) + min) }],
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}),
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);
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const transport = new StdioServerTransport();
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await server.connect(transport);
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```
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This is a simple example. Your MCP server runs inside the sandbox, so you can execute any code you'd like: query databases, call internal APIs, run shell commands, or interact with any service available in the container.
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</Step>
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<Step title="Package the MCP server">
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Bundle into a single JS file so it can be uploaded and executed without a `node_modules` folder.
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```bash
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npx esbuild src/mcp-server.ts --bundle --format=cjs --platform=node --target=node18 --minify --outfile=dist/mcp-server.cjs
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```
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This creates `dist/mcp-server.cjs` ready to upload.
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</Step>
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<Step title="Create sandbox and upload MCP server">
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Start your sandbox, then write the bundled file into it.
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<CodeGroup>
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```ts TypeScript
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import { SandboxAgent } from "sandbox-agent";
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import fs from "node:fs";
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const client = await SandboxAgent.connect({
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baseUrl: "http://127.0.0.1:2468",
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token: process.env.SANDBOX_TOKEN,
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});
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const content = await fs.promises.readFile("./dist/mcp-server.cjs");
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await client.writeFsFile(
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{ path: "/opt/mcp/custom-tools/mcp-server.cjs" },
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content,
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);
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```
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```bash cURL
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curl -X PUT "http://127.0.0.1:2468/v1/fs/file?path=/opt/mcp/custom-tools/mcp-server.cjs" \
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-H "Authorization: Bearer $SANDBOX_TOKEN" \
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--data-binary @./dist/mcp-server.cjs
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```
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</CodeGroup>
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</Step>
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<Step title="Create a session">
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Point an MCP server config at the bundled JS file. When the session starts, Sandbox Agent spawns the MCP server process and routes tool calls to it.
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<CodeGroup>
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```ts TypeScript
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await client.createSession("custom-tools", {
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agent: "claude",
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mcp: {
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customTools: {
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type: "local",
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command: ["node", "/opt/mcp/custom-tools/mcp-server.cjs"],
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},
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},
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});
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```
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```bash cURL
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curl -X POST "http://127.0.0.1:2468/v1/sessions/custom-tools" \
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-H "Authorization: Bearer $SANDBOX_TOKEN" \
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-H "Content-Type: application/json" \
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-d '{
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"agent": "claude",
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"mcp": {
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"customTools": {
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"type": "local",
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"command": ["node", "/opt/mcp/custom-tools/mcp-server.cjs"]
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}
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}
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}'
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```
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</CodeGroup>
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</Step>
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</Steps>
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## Option B: Tools via Skills
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Skills are markdown files that instruct the agent how to use a script. Upload the script and a skill file, then point the session at the skill directory.
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<Steps>
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<Step title="Write your script">
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Write a script that the agent will execute. This runs inside the sandbox just like an MCP server, but the agent invokes it directly via its shell tool.
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```ts src/random-number.ts
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const min = Number(process.argv[2]);
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const max = Number(process.argv[3]);
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if (Number.isNaN(min) || Number.isNaN(max)) {
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console.error("Usage: random-number <min> <max>");
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process.exit(1);
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}
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console.log(Math.floor(Math.random() * (max - min + 1)) + min);
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```
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</Step>
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<Step title="Write a skill file">
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Create a `SKILL.md` that tells the agent what the script does and how to run it. The frontmatter `name` and `description` fields are required. See [Skill authoring best practices](https://platform.claude.com/docs/en/agents-and-tools/agent-skills/best-practices) for tips on writing effective skills.
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```md SKILL.md
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---
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name: random-number
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description: Generate a random integer between min and max (inclusive). Use when the user asks for a random number.
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---
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To generate a random number, run:
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```bash
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node /opt/skills/random-number/random-number.cjs <min> <max>
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```
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This prints a single random integer between min and max (inclusive).
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</Step>
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<Step title="Package the script">
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Bundle the script just like an MCP server so it has no dependencies at runtime.
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```bash
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npx esbuild src/random-number.ts --bundle --format=cjs --platform=node --target=node18 --minify --outfile=dist/random-number.cjs
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```
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</Step>
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<Step title="Create sandbox and upload files">
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Upload both the bundled script and the skill file.
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<CodeGroup>
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```ts TypeScript
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import { SandboxAgent } from "sandbox-agent";
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import fs from "node:fs";
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const client = await SandboxAgent.connect({
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baseUrl: "http://127.0.0.1:2468",
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token: process.env.SANDBOX_TOKEN,
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});
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const script = await fs.promises.readFile("./dist/random-number.cjs");
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await client.writeFsFile(
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{ path: "/opt/skills/random-number/random-number.cjs" },
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script,
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);
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const skill = await fs.promises.readFile("./SKILL.md");
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await client.writeFsFile(
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{ path: "/opt/skills/random-number/SKILL.md" },
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skill,
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);
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```
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```bash cURL
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curl -X PUT "http://127.0.0.1:2468/v1/fs/file?path=/opt/skills/random-number/random-number.cjs" \
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-H "Authorization: Bearer $SANDBOX_TOKEN" \
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--data-binary @./dist/random-number.cjs
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curl -X PUT "http://127.0.0.1:2468/v1/fs/file?path=/opt/skills/random-number/SKILL.md" \
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-H "Authorization: Bearer $SANDBOX_TOKEN" \
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--data-binary @./SKILL.md
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```
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</CodeGroup>
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</Step>
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<Step title="Create a session">
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Point the session at the skill directory. The agent reads `SKILL.md` and learns how to use your script.
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<CodeGroup>
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```ts TypeScript
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await client.createSession("custom-tools", {
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agent: "claude",
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skills: {
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sources: [
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{ type: "local", source: "/opt/skills/random-number" },
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],
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},
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});
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```
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```bash cURL
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curl -X POST "http://127.0.0.1:2468/v1/sessions/custom-tools" \
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-H "Authorization: Bearer $SANDBOX_TOKEN" \
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-H "Content-Type: application/json" \
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-d '{
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"agent": "claude",
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"skills": {
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"sources": [
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{ "type": "local", "source": "/opt/skills/random-number" }
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]
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}
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}'
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```
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</CodeGroup>
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</Step>
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</Steps>
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## Notes
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- The sandbox image must include a Node.js runtime that can execute the bundled files.
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