AI prompts
base on A TypeScript framework for building MCP servers. # FastMCP
A TypeScript framework for building [MCP](https://glama.ai/mcp) servers capable of handling client sessions.
> [!NOTE]
>
> For a Python implementation, see [FastMCP](https://github.com/jlowin/fastmcp).
## Features
- Simple Tool, Resource, Prompt definition
- [Authentication](#authentication)
- [Sessions](#sessions)
- [Image content](#returning-an-image)
- [Audio content](#returning-an-audio)
- [Logging](#logging)
- [Error handling](#errors)
- [SSE](#sse)
- CORS (enabled by default)
- [Progress notifications](#progress)
- [Typed server events](#typed-server-events)
- [Prompt argument auto-completion](#prompt-argument-auto-completion)
- [Sampling](#requestsampling)
- Automated SSE pings
- Roots
- CLI for [testing](#test-with-mcp-cli) and [debugging](#inspect-with-mcp-inspector)
## Installation
```bash
npm install fastmcp
```
## Quickstart
> [!NOTE]
>
> There are many real-world examples of using FastMCP in the wild. See the [Showcase](#showcase) for examples.
```ts
import { FastMCP } from "fastmcp";
import { z } from "zod"; // Or any validation library that supports Standard Schema
const server = new FastMCP({
name: "My Server",
version: "1.0.0",
});
server.addTool({
name: "add",
description: "Add two numbers",
parameters: z.object({
a: z.number(),
b: z.number(),
}),
execute: async (args) => {
return String(args.a + args.b);
},
});
server.start({
transportType: "stdio",
});
```
_That's it!_ You have a working MCP server.
You can test the server in terminal with:
```bash
git clone https://github.com/punkpeye/fastmcp.git
cd fastmcp
pnpm install
pnpm build
# Test the addition server example using CLI:
npx fastmcp dev src/examples/addition.ts
# Test the addition server example using MCP Inspector:
npx fastmcp inspect src/examples/addition.ts
```
### SSE
[Server-Sent Events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events) (SSE) provide a mechanism for servers to send real-time updates to clients over an HTTPS connection. In the context of MCP, SSE is primarily used to enable remote MCP communication, allowing an MCP hosted on a remote machine to be accessed and relay updates over the network.
You can also run the server with SSE support:
```ts
server.start({
transportType: "sse",
sse: {
endpoint: "/sse",
port: 8080,
},
});
```
This will start the server and listen for SSE connections on `http://localhost:8080/sse`.
You can then use `SSEClientTransport` to connect to the server:
```ts
import { SSEClientTransport } from "@modelcontextprotocol/sdk/client/sse.js";
const client = new Client(
{
name: "example-client",
version: "1.0.0",
},
{
capabilities: {},
},
);
const transport = new SSEClientTransport(new URL(`http://localhost:8080/sse`));
await client.connect(transport);
```
## Core Concepts
### Tools
[Tools](https://modelcontextprotocol.io/docs/concepts/tools) in MCP allow servers to expose executable functions that can be invoked by clients and used by LLMs to perform actions.
FastMCP uses the [Standard Schema](https://standardschema.dev) specification for defining tool parameters. This allows you to use your preferred schema validation library (like Zod, ArkType, or Valibot) as long as it implements the spec.
**Zod Example:**
```typescript
import { z } from "zod";
server.addTool({
name: "fetch-zod",
description: "Fetch the content of a url (using Zod)",
parameters: z.object({
url: z.string(),
}),
execute: async (args) => {
return await fetchWebpageContent(args.url);
},
});
```
**ArkType Example:**
```typescript
import { type } from "arktype";
server.addTool({
name: "fetch-arktype",
description: "Fetch the content of a url (using ArkType)",
parameters: type({
url: "string",
}),
execute: async (args) => {
return await fetchWebpageContent(args.url);
},
});
```
**Valibot Example:**
Valibot requires the peer dependency @valibot/to-json-schema.
```typescript
import * as v from "valibot";
server.addTool({
name: "fetch-valibot",
description: "Fetch the content of a url (using Valibot)",
parameters: v.object({
url: v.string(),
}),
execute: async (args) => {
return await fetchWebpageContent(args.url);
},
});
```
#### Returning a string
`execute` can return a string:
```js
server.addTool({
name: "download",
description: "Download a file",
parameters: z.object({
url: z.string(),
}),
execute: async (args) => {
return "Hello, world!";
},
});
```
The latter is equivalent to:
```js
server.addTool({
name: "download",
description: "Download a file",
parameters: z.object({
url: z.string(),
}),
execute: async (args) => {
return {
content: [
{
type: "text",
text: "Hello, world!",
},
],
};
},
});
```
#### Returning a list
If you want to return a list of messages, you can return an object with a `content` property:
```js
server.addTool({
name: "download",
description: "Download a file",
parameters: z.object({
url: z.string(),
}),
execute: async (args) => {
return {
content: [
{ type: "text", text: "First message" },
{ type: "text", text: "Second message" },
],
};
},
});
```
#### Returning an image
Use the `imageContent` to create a content object for an image:
```js
import { imageContent } from "fastmcp";
server.addTool({
name: "download",
description: "Download a file",
parameters: z.object({
url: z.string(),
}),
execute: async (args) => {
return imageContent({
url: "https://example.com/image.png",
});
// or...
// return imageContent({
// path: "/path/to/image.png",
// });
// or...
// return imageContent({
// buffer: Buffer.from("iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=", "base64"),
// });
// or...
// return {
// content: [
// await imageContent(...)
// ],
// };
},
});
```
The `imageContent` function takes the following options:
- `url`: The URL of the image.
- `path`: The path to the image file.
- `buffer`: The image data as a buffer.
Only one of `url`, `path`, or `buffer` must be specified.
The above example is equivalent to:
```js
server.addTool({
name: "download",
description: "Download a file",
parameters: z.object({
url: z.string(),
}),
execute: async (args) => {
return {
content: [
{
type: "image",
data: "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=",
mimeType: "image/png",
},
],
};
},
});
```
#### Returning an audio
Use the `audioContent` to create a content object for an audio:
```js
import { audioContent } from "fastmcp";
server.addTool({
name: "download",
description: "Download a file",
parameters: z.object({
url: z.string(),
}),
execute: async (args) => {
return audioContent({
url: "https://example.com/audio.mp3",
});
// or...
// return audioContent({
// path: "/path/to/audio.mp3",
// });
// or...
// return audioContent({
// buffer: Buffer.from("iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=", "base64"),
// });
// or...
// return {
// content: [
// await audioContent(...)
// ],
// };
},
});
```
The `audioContent` function takes the following options:
- `url`: The URL of the audio.
- `path`: The path to the audio file.
- `buffer`: The audio data as a buffer.
Only one of `url`, `path`, or `buffer` must be specified.
The above example is equivalent to:
```js
server.addTool({
name: "download",
description: "Download a file",
parameters: z.object({
url: z.string(),
}),
execute: async (args) => {
return {
content: [
{
type: "audio",
data: "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=",
mimeType: "audio/mpeg",
},
],
};
},
});
```
#### Return combination type
You can combine various types in this way and send them back to AI
```js
server.addTool({
name: "download",
description: "Download a file",
parameters: z.object({
url: z.string(),
}),
execute: async (args) => {
return {
content: [
{
type: "text",
text: "Hello, world!",
},
{
type: "image",
data: "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=",
mimeType: "image/png",
},
{
type: "audio",
data: "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=",
mimeType: "audio/mpeg",
},
],
};
},
// or...
// execute: async (args) => {
// const imgContent = imageContent({
// url: "https://example.com/image.png",
// });
// const audContent = audioContent({
// url: "https://example.com/audio.mp3",
// });
// return {
// content: [
// {
// type: "text",
// text: "Hello, world!",
// },
// imgContent,
// audContent,
// ],
// };
// },
});
```
#### Logging
Tools can log messages to the client using the `log` object in the context object:
```js
server.addTool({
name: "download",
description: "Download a file",
parameters: z.object({
url: z.string(),
}),
execute: async (args, { log }) => {
log.info("Downloading file...", {
url,
});
// ...
log.info("Downloaded file");
return "done";
},
});
```
The `log` object has the following methods:
- `debug(message: string, data?: SerializableValue)`
- `error(message: string, data?: SerializableValue)`
- `info(message: string, data?: SerializableValue)`
- `warn(message: string, data?: SerializableValue)`
#### Errors
The errors that are meant to be shown to the user should be thrown as `UserError` instances:
```js
import { UserError } from "fastmcp";
server.addTool({
name: "download",
description: "Download a file",
parameters: z.object({
url: z.string(),
}),
execute: async (args) => {
if (args.url.startsWith("https://example.com")) {
throw new UserError("This URL is not allowed");
}
return "done";
},
});
```
#### Progress
Tools can report progress by calling `reportProgress` in the context object:
```js
server.addTool({
name: "download",
description: "Download a file",
parameters: z.object({
url: z.string(),
}),
execute: async (args, { reportProgress }) => {
reportProgress({
progress: 0,
total: 100,
});
// ...
reportProgress({
progress: 100,
total: 100,
});
return "done";
},
});
```
#### Tool Annotations
As of the MCP Specification (2025-03-26), tools can include annotations that provide richer context and control by adding metadata about a tool's behavior:
```typescript
server.addTool({
name: "fetch-content",
description: "Fetch content from a URL",
parameters: z.object({
url: z.string(),
}),
annotations: {
title: "Web Content Fetcher", // Human-readable title for UI display
readOnlyHint: true, // Tool doesn't modify its environment
openWorldHint: true, // Tool interacts with external entities
},
execute: async (args) => {
return await fetchWebpageContent(args.url);
},
});
```
The available annotations are:
| Annotation | Type | Default | Description |
| :---------------- | :------ | :------ | :----------------------------------------------------------------------------------------------------------------------------------- |
| `title` | string | - | A human-readable title for the tool, useful for UI display |
| `readOnlyHint` | boolean | `false` | If true, indicates the tool does not modify its environment |
| `destructiveHint` | boolean | `true` | If true, the tool may perform destructive updates (only meaningful when `readOnlyHint` is false) |
| `idempotentHint` | boolean | `false` | If true, calling the tool repeatedly with the same arguments has no additional effect (only meaningful when `readOnlyHint` is false) |
| `openWorldHint` | boolean | `true` | If true, the tool may interact with an "open world" of external entities |
These annotations help clients and LLMs better understand how to use the tools and what to expect when calling them.
### Resources
[Resources](https://modelcontextprotocol.io/docs/concepts/resources) represent any kind of data that an MCP server wants to make available to clients. This can include:
- File contents
- Screenshots and images
- Log files
- And more
Each resource is identified by a unique URI and can contain either text or binary data.
```ts
server.addResource({
uri: "file:///logs/app.log",
name: "Application Logs",
mimeType: "text/plain",
async load() {
return {
text: await readLogFile(),
};
},
});
```
> [!NOTE]
>
> `load` can return multiple resources. This could be used, for example, to return a list of files inside a directory when the directory is read.
>
> ```ts
> async load() {
> return [
> {
> text: "First file content",
> },
> {
> text: "Second file content",
> },
> ];
> }
> ```
You can also return binary contents in `load`:
```ts
async load() {
return {
blob: 'base64-encoded-data'
};
}
```
### Resource templates
You can also define resource templates:
```ts
server.addResourceTemplate({
uriTemplate: "file:///logs/{name}.log",
name: "Application Logs",
mimeType: "text/plain",
arguments: [
{
name: "name",
description: "Name of the log",
required: true,
},
],
async load({ name }) {
return {
text: `Example log content for ${name}`,
};
},
});
```
#### Resource template argument auto-completion
Provide `complete` functions for resource template arguments to enable automatic completion:
```ts
server.addResourceTemplate({
uriTemplate: "file:///logs/{name}.log",
name: "Application Logs",
mimeType: "text/plain",
arguments: [
{
name: "name",
description: "Name of the log",
required: true,
complete: async (value) => {
if (value === "Example") {
return {
values: ["Example Log"],
};
}
return {
values: [],
};
},
},
],
async load({ name }) {
return {
text: `Example log content for ${name}`,
};
},
});
```
### Prompts
[Prompts](https://modelcontextprotocol.io/docs/concepts/prompts) enable servers to define reusable prompt templates and workflows that clients can easily surface to users and LLMs. They provide a powerful way to standardize and share common LLM interactions.
```ts
server.addPrompt({
name: "git-commit",
description: "Generate a Git commit message",
arguments: [
{
name: "changes",
description: "Git diff or description of changes",
required: true,
},
],
load: async (args) => {
return `Generate a concise but descriptive commit message for these changes:\n\n${args.changes}`;
},
});
```
#### Prompt argument auto-completion
Prompts can provide auto-completion for their arguments:
```js
server.addPrompt({
name: "countryPoem",
description: "Writes a poem about a country",
load: async ({ name }) => {
return `Hello, ${name}!`;
},
arguments: [
{
name: "name",
description: "Name of the country",
required: true,
complete: async (value) => {
if (value === "Germ") {
return {
values: ["Germany"],
};
}
return {
values: [],
};
},
},
],
});
```
#### Prompt argument auto-completion using `enum`
If you provide an `enum` array for an argument, the server will automatically provide completions for the argument.
```js
server.addPrompt({
name: "countryPoem",
description: "Writes a poem about a country",
load: async ({ name }) => {
return `Hello, ${name}!`;
},
arguments: [
{
name: "name",
description: "Name of the country",
required: true,
enum: ["Germany", "France", "Italy"],
},
],
});
```
### Authentication
FastMCP allows you to `authenticate` clients using a custom function:
```ts
import { AuthError } from "fastmcp";
const server = new FastMCP({
name: "My Server",
version: "1.0.0",
authenticate: ({ request }) => {
const apiKey = request.headers["x-api-key"];
if (apiKey !== "123") {
throw new Response(null, {
status: 401,
statusText: "Unauthorized",
});
}
// Whatever you return here will be accessible in the `context.session` object.
return {
id: 1,
};
},
});
```
Now you can access the authenticated session data in your tools:
```ts
server.addTool({
name: "sayHello",
execute: async (args, { session }) => {
return `Hello, ${session.id}!`;
},
});
```
### Providing Instructions
You can provide instructions to the server using the `instructions` option:
```ts
const server = new FastMCP({
name: "My Server",
version: "1.0.0",
instructions:
'Instructions describing how to use the server and its features.\n\nThis can be used by clients to improve the LLM\'s understanding of available tools, resources, etc. It can be thought of like a "hint" to the model. For example, this information MAY be added to the system prompt.',
});
```
### Sessions
The `session` object is an instance of `FastMCPSession` and it describes active client sessions.
```ts
server.sessions;
```
We allocate a new server instance for each client connection to enable 1:1 communication between a client and the server.
### Typed server events
You can listen to events emitted by the server using the `on` method:
```ts
server.on("connect", (event) => {
console.log("Client connected:", event.session);
});
server.on("disconnect", (event) => {
console.log("Client disconnected:", event.session);
});
```
## `FastMCPSession`
`FastMCPSession` represents a client session and provides methods to interact with the client.
Refer to [Sessions](#sessions) for examples of how to obtain a `FastMCPSession` instance.
### `requestSampling`
`requestSampling` creates a [sampling](https://modelcontextprotocol.io/docs/concepts/sampling) request and returns the response.
```ts
await session.requestSampling({
messages: [
{
role: "user",
content: {
type: "text",
text: "What files are in the current directory?",
},
},
],
systemPrompt: "You are a helpful file system assistant.",
includeContext: "thisServer",
maxTokens: 100,
});
```
### `clientCapabilities`
The `clientCapabilities` property contains the client capabilities.
```ts
session.clientCapabilities;
```
### `loggingLevel`
The `loggingLevel` property describes the logging level as set by the client.
```ts
session.loggingLevel;
```
### `roots`
The `roots` property contains the roots as set by the client.
```ts
session.roots;
```
### `server`
The `server` property contains an instance of MCP server that is associated with the session.
```ts
session.server;
```
### Typed session events
You can listen to events emitted by the session using the `on` method:
```ts
session.on("rootsChanged", (event) => {
console.log("Roots changed:", event.roots);
});
session.on("error", (event) => {
console.error("Error:", event.error);
});
```
## Running Your Server
### Test with `mcp-cli`
The fastest way to test and debug your server is with `fastmcp dev`:
```bash
npx fastmcp dev server.js
npx fastmcp dev server.ts
```
This will run your server with [`mcp-cli`](https://github.com/wong2/mcp-cli) for testing and debugging your MCP server in the terminal.
### Inspect with `MCP Inspector`
Another way is to use the official [`MCP Inspector`](https://modelcontextprotocol.io/docs/tools/inspector) to inspect your server with a Web UI:
```bash
npx fastmcp inspect server.ts
```
## FAQ
### How to use with Claude Desktop?
Follow the guide https://modelcontextprotocol.io/quickstart/user and add the following configuration:
```json
{
"mcpServers": {
"my-mcp-server": {
"command": "npx",
"args": ["tsx", "/PATH/TO/YOUR_PROJECT/src/index.ts"],
"env": {
"YOUR_ENV_VAR": "value"
}
}
}
}
```
## Showcase
> [!NOTE]
>
> If you've developed a server using FastMCP, please [submit a PR](https://github.com/punkpeye/fastmcp) to showcase it here!
- [apinetwork/piapi-mcp-server](https://github.com/apinetwork/piapi-mcp-server) - generate media using Midjourney/Flux/Kling/LumaLabs/Udio/Chrip/Trellis
- [domdomegg/computer-use-mcp](https://github.com/domdomegg/computer-use-mcp) - controls your computer
- [LiterallyBlah/Dradis-MCP](https://github.com/LiterallyBlah/Dradis-MCP) – manages projects and vulnerabilities in Dradis
- [Meeting-Baas/meeting-mcp](https://github.com/Meeting-Baas/meeting-mcp) - create meeting bots, search transcripts, and manage recording data
- [drumnation/unsplash-smart-mcp-server](https://github.com/drumnation/unsplash-smart-mcp-server) – enables AI agents to seamlessly search, recommend, and deliver professional stock photos from Unsplash
- [ssmanji89/halopsa-workflows-mcp](https://github.com/ssmanji89/halopsa-workflows-mcp) - HaloPSA Workflows integration with AI assistants
- [aiamblichus/mcp-chat-adapter](https://github.com/aiamblichus/mcp-chat-adapter) – provides a clean interface for LLMs to use chat completion
- [cswkim/discogs-mcp-server](https://github.com/cswkim/discogs-mcp-server) - connects to the Discogs API for interacting with your music collection
## Acknowledgements
- FastMCP is inspired by the [Python implementation](https://github.com/jlowin/fastmcp) by [Jonathan Lowin](https://github.com/jlowin).
- Parts of codebase were adopted from [LiteMCP](https://github.com/wong2/litemcp).
- Parts of codebase were adopted from [Model Context protocolでSSEをやってみる](https://dev.classmethod.jp/articles/mcp-sse/).
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