# MCP servers with the Gemini CLI

This document provides a guide to configuring and using Model Context Protocol
(MCP) servers with the Gemini CLI.

## What is an MCP server?

An MCP server is an application that exposes tools and resources to the Gemini
CLI through the Model Context Protocol, allowing it to interact with external
systems and data sources. MCP servers act as a bridge between the Gemini model
and your local environment or other services like APIs.

An MCP server enables the Gemini CLI to:

- **Discover tools:** List available tools, their descriptions, and parameters
  through standardized schema definitions.
- **Execute tools:** Call specific tools with defined arguments and receive
  structured responses.
- **Access resources:** Read data from specific resources that the server
  exposes (files, API payloads, reports, etc.).

With an MCP server, you can extend the Gemini CLI's capabilities to perform
actions beyond its built-in features, such as interacting with databases, APIs,
custom scripts, or specialized workflows.

## Core integration architecture

The Gemini CLI integrates with MCP servers through a sophisticated discovery and
execution system built into the core package (`packages/core/src/tools/`):

### Discovery Layer (`mcp-client.ts`)

The discovery process is orchestrated by `discoverMcpTools()`, which:

1. **Iterates through configured servers** from your `settings.json`
   `mcpServers` configuration
2. **Establishes connections** using appropriate transport mechanisms (Stdio,
   SSE, or Streamable HTTP)
3. **Fetches tool definitions** from each server using the MCP protocol
4. **Sanitizes and validates** tool schemas for compatibility with the Gemini
   API
5. **Registers tools** in the global tool registry with conflict resolution
6. **Fetches and registers resources** if the server exposes any

### Execution layer (`mcp-tool.ts`)

Each discovered MCP tool is wrapped in a `DiscoveredMCPTool` instance that:

- **Handles confirmation logic** based on server trust settings and user
  preferences
- **Manages tool execution** by calling the MCP server with proper parameters
- **Processes responses** for both the LLM context and user display
- **Maintains connection state** and handles timeouts

### Transport mechanisms

The Gemini CLI supports three MCP transport types:

- **Stdio Transport:** Spawns a subprocess and communicates via stdin/stdout
- **SSE Transport:** Connects to Server-Sent Events endpoints
- **Streamable HTTP Transport:** Uses HTTP streaming for communication

## Working with MCP resources

Some MCP servers expose contextual “resources” in addition to the tools and
prompts. Gemini CLI discovers these automatically and gives you the possibility
to reference them in the chat.

### Discovery and listing

- When discovery runs, the CLI fetches each server’s `resources/list` results.
- The `/mcp` command displays a Resources section alongside Tools and Prompts
  for every connected server.

This returns a concise, plain-text list of URIs plus metadata.

### Referencing resources in a conversation

You can use the same `@` syntax already known for referencing local files:

```
@server://resource/path
```

Resource URIs appear in the completion menu together with filesystem paths. When
you submit the message, the CLI calls `resources/read` and injects the content
in the conversation.

## How to set up your MCP server

The Gemini CLI uses the `mcpServers` configuration in your `settings.json` file
to locate and connect to MCP servers. This configuration supports multiple
servers with different transport mechanisms.

### Configure the MCP server in settings.json

You can configure MCP servers in your `settings.json` file in two main ways:
through the top-level `mcpServers` object for specific server definitions, and
through the `mcp` object for global settings that control server discovery and
execution.

#### Global MCP settings (`mcp`)

The `mcp` object in your `settings.json` allows you to define global rules for
all MCP servers.

- **`mcp.serverCommand`** (string): A global command to start an MCP server.
- **`mcp.allowed`** (array of strings): A list of MCP server names to allow. If
  this is set, only servers from this list (matching the keys in the
  `mcpServers` object) will be connected to.
- **`mcp.excluded`** (array of strings): A list of MCP server names to exclude.
  Servers in this list will not be connected to.

**Example:**

```json
{
  "mcp": {
    "allowed": ["my-trusted-server"],
    "excluded": ["experimental-server"]
  }
}
```

#### Server-specific configuration (`mcpServers`)

The `mcpServers` object is where you define each individual MCP server you want
the CLI to connect to.

### Configuration structure

Add an `mcpServers` object to your `settings.json` file:

```json
{ ...file contains other config objects
  "mcpServers": {
    "serverName": {
      "command": "path/to/server",
      "args": ["--arg1", "value1"],
      "env": {
        "API_KEY": "$MY_API_TOKEN"
      },
      "cwd": "./server-directory",
      "timeout": 30000,
      "trust": false
    }
  }
}
```

### Configuration properties

Each server configuration supports the following properties:

#### Required (one of the following)

- **`command`** (string): Path to the executable for Stdio transport
- **`url`** (string): SSE endpoint URL (e.g., `"http://localhost:8080/sse"`)
- **`httpUrl`** (string): HTTP streaming endpoint URL

#### Optional

- **`args`** (string[]): Command-line arguments for Stdio transport
- **`headers`** (object): Custom HTTP headers when using `url` or `httpUrl`
- **`env`** (object): Environment variables for the server process. Values can
  reference environment variables using `$VAR_NAME` or `${VAR_NAME}` syntax
- **`cwd`** (string): Working directory for Stdio transport
- **`timeout`** (number): Request timeout in milliseconds (default: 600,000ms =
  10 minutes)
- **`trust`** (boolean): When `true`, bypasses all tool call confirmations for
  this server (default: `false`)
- **`includeTools`** (string[]): List of tool names to include from this MCP
  server. When specified, only the tools listed here will be available from this
  server (allowlist behavior). If not specified, all tools from the server are
  enabled by default.
- **`excludeTools`** (string[]): List of tool names to exclude from this MCP
  server. Tools listed here will not be available to the model, even if they are
  exposed by the server. **Note:** `excludeTools` takes precedence over
  `includeTools` - if a tool is in both lists, it will be excluded.
- **`targetAudience`** (string): The OAuth Client ID allowlisted on the
  IAP-protected application you are trying to access. Used with
  `authProviderType: 'service_account_impersonation'`.
- **`targetServiceAccount`** (string): The email address of the Google Cloud
  Service Account to impersonate. Used with
  `authProviderType: 'service_account_impersonation'`.

### OAuth support for remote MCP servers

The Gemini CLI supports OAuth 2.0 authentication for remote MCP servers using
SSE or HTTP transports. This enables secure access to MCP servers that require
authentication.

#### Automatic OAuth discovery

For servers that support OAuth discovery, you can omit the OAuth configuration
and let the CLI discover it automatically:

```json
{
  "mcpServers": {
    "discoveredServer": {
      "url": "https://api.example.com/sse"
    }
  }
}
```

The CLI will automatically:

- Detect when a server requires OAuth authentication (401 responses)
- Discover OAuth endpoints from server metadata
- Perform dynamic client registration if supported
- Handle the OAuth flow and token management

#### Authentication flow

When connecting to an OAuth-enabled server:

1. **Initial connection attempt** fails with 401 Unauthorized
2. **OAuth discovery** finds authorization and token endpoints
3. **Browser opens** for user authentication (requires local browser access)
4. **Authorization code** is exchanged for access tokens
5. **Tokens are stored** securely for future use
6. **Connection retry** succeeds with valid tokens

#### Browser redirect requirements

**Important:** OAuth authentication requires that your local machine can:

- Open a web browser for authentication
- Receive redirects on `http://localhost:7777/oauth/callback`

This feature will not work in:

- Headless environments without browser access
- Remote SSH sessions without X11 forwarding
- Containerized environments without browser support

#### Managing OAuth authentication

Use the `/mcp auth` command to manage OAuth authentication:

```bash
# List servers requiring authentication
/mcp auth

# Authenticate with a specific server
/mcp auth serverName

# Re-authenticate if tokens expire
/mcp auth serverName
```

#### OAuth configuration properties

- **`enabled`** (boolean): Enable OAuth for this server
- **`clientId`** (string): OAuth client identifier (optional with dynamic
  registration)
- **`clientSecret`** (string): OAuth client secret (optional for public clients)
- **`authorizationUrl`** (string): OAuth authorization endpoint (auto-discovered
  if omitted)
- **`tokenUrl`** (string): OAuth token endpoint (auto-discovered if omitted)
- **`scopes`** (string[]): Required OAuth scopes
- **`redirectUri`** (string): Custom redirect URI (defaults to
  `http://localhost:7777/oauth/callback`)
- **`tokenParamName`** (string): Query parameter name for tokens in SSE URLs
- **`audiences`** (string[]): Audiences the token is valid for

#### Token management

OAuth tokens are automatically:

- **Stored securely** in `~/.gemini/mcp-oauth-tokens.json`
- **Refreshed** when expired (if refresh tokens are available)
- **Validated** before each connection attempt
- **Cleaned up** when invalid or expired

#### Authentication provider type

You can specify the authentication provider type using the `authProviderType`
property:

- **`authProviderType`** (string): Specifies the authentication provider. Can be
  one of the following:
  - **`dynamic_discovery`** (default): The CLI will automatically discover the
    OAuth configuration from the server.
  - **`google_credentials`**: The CLI will use the Google Application Default
    Credentials (ADC) to authenticate with the server. When using this provider,
    you must specify the required scopes.
  - **`service_account_impersonation`**: The CLI will impersonate a Google Cloud
    Service Account to authenticate with the server. This is useful for
    accessing IAP-protected services (this was specifically designed for Cloud
    Run services).

#### Google credentials

```json
{
  "mcpServers": {
    "googleCloudServer": {
      "httpUrl": "https://my-gcp-service.run.app/mcp",
      "authProviderType": "google_credentials",
      "oauth": {
        "scopes": ["https://www.googleapis.com/auth/userinfo.email"]
      }
    }
  }
}
```

#### Service account impersonation

To authenticate with a server using Service Account Impersonation, you must set
the `authProviderType` to `service_account_impersonation` and provide the
following properties:

- **`targetAudience`** (string): The OAuth Client ID allowslisted on the
  IAP-protected application you are trying to access.
- **`targetServiceAccount`** (string): The email address of the Google Cloud
  Service Account to impersonate.

The CLI will use your local Application Default Credentials (ADC) to generate an
OIDC ID token for the specified service account and audience. This token will
then be used to authenticate with the MCP server.

#### Setup instructions

1. **[Create](https://cloud.google.com/iap/docs/oauth-client-creation) or use an
   existing OAuth 2.0 client ID.** To use an existing OAuth 2.0 client ID,
   follow the steps in
   [How to share OAuth Clients](https://cloud.google.com/iap/docs/sharing-oauth-clients).
2. **Add the OAuth ID to the allowlist for
   [programmatic access](https://cloud.google.com/iap/docs/sharing-oauth-clients#programmatic_access)
   for the application.** Since Cloud Run is not yet a supported resource type
   in gcloud iap, you must allowlist the Client ID on the project.
3. **Create a service account.**
   [Documentation](https://cloud.google.com/iam/docs/service-accounts-create#creating),
   [Cloud Console Link](https://console.cloud.google.com/iam-admin/serviceaccounts)
4. **Add both the service account and users to the IAP Policy** in the
   "Security" tab of the Cloud Run service itself or via gcloud.
5. **Grant all users and groups** who will access the MCP Server the necessary
   permissions to
   [impersonate the service account](https://cloud.google.com/docs/authentication/use-service-account-impersonation)
   (i.e., `roles/iam.serviceAccountTokenCreator`).
6. **[Enable](https://console.cloud.google.com/apis/library/iamcredentials.googleapis.com)
   the IAM Credentials API** for your project.

### Example configurations

#### Python MCP server (stdio)

```json
{
  "mcpServers": {
    "pythonTools": {
      "command": "python",
      "args": ["-m", "my_mcp_server", "--port", "8080"],
      "cwd": "./mcp-servers/python",
      "env": {
        "DATABASE_URL": "$DB_CONNECTION_STRING",
        "API_KEY": "${EXTERNAL_API_KEY}"
      },
      "timeout": 15000
    }
  }
}
```

#### Node.js MCP server (stdio)

```json
{
  "mcpServers": {
    "nodeServer": {
      "command": "node",
      "args": ["dist/server.js", "--verbose"],
      "cwd": "./mcp-servers/node",
      "trust": true
    }
  }
}
```

#### Docker-based MCP server

```json
{
  "mcpServers": {
    "dockerizedServer": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "API_KEY",
        "-v",
        "${PWD}:/workspace",
        "my-mcp-server:latest"
      ],
      "env": {
        "API_KEY": "$EXTERNAL_SERVICE_TOKEN"
      }
    }
  }
}
```

#### HTTP-based MCP server

```json
{
  "mcpServers": {
    "httpServer": {
      "httpUrl": "http://localhost:3000/mcp",
      "timeout": 5000
    }
  }
}
```

#### HTTP-based MCP Server with custom headers

```json
{
  "mcpServers": {
    "httpServerWithAuth": {
      "httpUrl": "http://localhost:3000/mcp",
      "headers": {
        "Authorization": "Bearer your-api-token",
        "X-Custom-Header": "custom-value",
        "Content-Type": "application/json"
      },
      "timeout": 5000
    }
  }
}
```

#### MCP server with tool filtering

```json
{
  "mcpServers": {
    "filteredServer": {
      "command": "python",
      "args": ["-m", "my_mcp_server"],
      "includeTools": ["safe_tool", "file_reader", "data_processor"],
      // "excludeTools": ["dangerous_tool", "file_deleter"],
      "timeout": 30000
    }
  }
}
```

### SSE MCP server with SA impersonation

```json
{
  "mcpServers": {
    "myIapProtectedServer": {
      "url": "https://my-iap-service.run.app/sse",
      "authProviderType": "service_account_impersonation",
      "targetAudience": "YOUR_IAP_CLIENT_ID.apps.googleusercontent.com",
      "targetServiceAccount": "your-sa@your-project.iam.gserviceaccount.com"
    }
  }
}
```

## Discovery process deep dive

When the Gemini CLI starts, it performs MCP server discovery through the
following detailed process:

### 1. Server iteration and connection

For each configured server in `mcpServers`:

1. **Status tracking begins:** Server status is set to `CONNECTING`
2. **Transport selection:** Based on configuration properties:
   - `httpUrl` → `StreamableHTTPClientTransport`
   - `url` → `SSEClientTransport`
   - `command` → `StdioClientTransport`
3. **Connection establishment:** The MCP client attempts to connect with the
   configured timeout
4. **Error handling:** Connection failures are logged and the server status is
   set to `DISCONNECTED`

### 2. Tool discovery

Upon successful connection:

1. **Tool listing:** The client calls the MCP server's tool listing endpoint
2. **Schema validation:** Each tool's function declaration is validated
3. **Tool filtering:** Tools are filtered based on `includeTools` and
   `excludeTools` configuration
4. **Name sanitization:** Tool names are cleaned to meet Gemini API
   requirements:
   - Invalid characters (non-alphanumeric, underscore, dot, hyphen) are replaced
     with underscores
   - Names longer than 63 characters are truncated with middle replacement
     (`___`)

### 3. Conflict resolution

When multiple servers expose tools with the same name:

1. **First registration wins:** The first server to register a tool name gets
   the unprefixed name
2. **Automatic prefixing:** Subsequent servers get prefixed names:
   `serverName__toolName`
3. **Registry tracking:** The tool registry maintains mappings between server
   names and their tools

### 4. Schema processing

Tool parameter schemas undergo sanitization for Gemini API compatibility:

- **`$schema` properties** are removed
- **`additionalProperties`** are stripped
- **`anyOf` with `default`** have their default values removed (Vertex AI
  compatibility)
- **Recursive processing** applies to nested schemas

### 5. Connection management

After discovery:

- **Persistent connections:** Servers that successfully register tools maintain
  their connections
- **Cleanup:** Servers that provide no usable tools have their connections
  closed
- **Status updates:** Final server statuses are set to `CONNECTED` or
  `DISCONNECTED`

## Tool execution flow

When the Gemini model decides to use an MCP tool, the following execution flow
occurs:

### 1. Tool invocation

The model generates a `FunctionCall` with:

- **Tool name:** The registered name (potentially prefixed)
- **Arguments:** JSON object matching the tool's parameter schema

### 2. Confirmation process

Each `DiscoveredMCPTool` implements sophisticated confirmation logic:

#### Trust-based bypass

```typescript
if (this.trust) {
  return false; // No confirmation needed
}
```

#### Dynamic allow-listing

The system maintains internal allow-lists for:

- **Server-level:** `serverName` → All tools from this server are trusted
- **Tool-level:** `serverName.toolName` → This specific tool is trusted

#### User choice handling

When confirmation is required, users can choose:

- **Proceed once:** Execute this time only
- **Always allow this tool:** Add to tool-level allow-list
- **Always allow this server:** Add to server-level allow-list
- **Cancel:** Abort execution

### 3. Execution

Upon confirmation (or trust bypass):

1. **Parameter preparation:** Arguments are validated against the tool's schema
2. **MCP call:** The underlying `CallableTool` invokes the server with:

   ```typescript
   const functionCalls = [
     {
       name: this.serverToolName, // Original server tool name
       args: params,
     },
   ];
   ```

3. **Response processing:** Results are formatted for both LLM context and user
   display

### 4. Response handling

The execution result contains:

- **`llmContent`:** Raw response parts for the language model's context
- **`returnDisplay`:** Formatted output for user display (often JSON in markdown
  code blocks)

## How to interact with your MCP server

### Using the `/mcp` command

The `/mcp` command provides comprehensive information about your MCP server
setup:

```bash
/mcp
```

This displays:

- **Server list:** All configured MCP servers
- **Connection status:** `CONNECTED`, `CONNECTING`, or `DISCONNECTED`
- **Server details:** Configuration summary (excluding sensitive data)
- **Available tools:** List of tools from each server with descriptions
- **Discovery state:** Overall discovery process status

### Example `/mcp` output

```
MCP Servers Status:

📡 pythonTools (CONNECTED)
  Command: python -m my_mcp_server --port 8080
  Working Directory: ./mcp-servers/python
  Timeout: 15000ms
  Tools: calculate_sum, file_analyzer, data_processor

🔌 nodeServer (DISCONNECTED)
  Command: node dist/server.js --verbose
  Error: Connection refused

🐳 dockerizedServer (CONNECTED)
  Command: docker run -i --rm -e API_KEY my-mcp-server:latest
  Tools: docker__deploy, docker__status

Discovery State: COMPLETED
```

### Tool usage

Once discovered, MCP tools are available to the Gemini model like built-in
tools. The model will automatically:

1. **Select appropriate tools** based on your requests
2. **Present confirmation dialogs** (unless the server is trusted)
3. **Execute tools** with proper parameters
4. **Display results** in a user-friendly format

## Status monitoring and troubleshooting

### Connection states

The MCP integration tracks several states:

#### Server status (`MCPServerStatus`)

- **`DISCONNECTED`:** Server is not connected or has errors
- **`CONNECTING`:** Connection attempt in progress
- **`CONNECTED`:** Server is connected and ready

#### Discovery state (`MCPDiscoveryState`)

- **`NOT_STARTED`:** Discovery hasn't begun
- **`IN_PROGRESS`:** Currently discovering servers
- **`COMPLETED`:** Discovery finished (with or without errors)

### Common issues and solutions

#### Server won't connect

**Symptoms:** Server shows `DISCONNECTED` status

**Troubleshooting:**

1. **Check configuration:** Verify `command`, `args`, and `cwd` are correct
2. **Test manually:** Run the server command directly to ensure it works
3. **Check dependencies:** Ensure all required packages are installed
4. **Review logs:** Look for error messages in the CLI output
5. **Verify permissions:** Ensure the CLI can execute the server command

#### No tools discovered

**Symptoms:** Server connects but no tools are available

**Troubleshooting:**

1. **Verify tool registration:** Ensure your server actually registers tools
2. **Check MCP protocol:** Confirm your server implements the MCP tool listing
   correctly
3. **Review server logs:** Check stderr output for server-side errors
4. **Test tool listing:** Manually test your server's tool discovery endpoint

#### Tools not executing

**Symptoms:** Tools are discovered but fail during execution

**Troubleshooting:**

1. **Parameter validation:** Ensure your tool accepts the expected parameters
2. **Schema compatibility:** Verify your input schemas are valid JSON Schema
3. **Error handling:** Check if your tool is throwing unhandled exceptions
4. **Timeout issues:** Consider increasing the `timeout` setting

#### Sandbox compatibility

**Symptoms:** MCP servers fail when sandboxing is enabled

**Solutions:**

1. **Docker-based servers:** Use Docker containers that include all dependencies
2. **Path accessibility:** Ensure server executables are available in the
   sandbox
3. **Network access:** Configure sandbox to allow necessary network connections
4. **Environment variables:** Verify required environment variables are passed
   through

### Debugging tips

1. **Enable debug mode:** Run the CLI with `--debug` for verbose output (use F12
   to open debug console in interactive mode)
2. **Check stderr:** MCP server stderr is captured and logged (INFO messages
   filtered)
3. **Test isolation:** Test your MCP server independently before integrating
4. **Incremental setup:** Start with simple tools before adding complex
   functionality
5. **Use `/mcp` frequently:** Monitor server status during development

## Important notes

### Security sonsiderations

- **Trust settings:** The `trust` option bypasses all confirmation dialogs. Use
  cautiously and only for servers you completely control
- **Access tokens:** Be security-aware when configuring environment variables
  containing API keys or tokens
- **Sandbox compatibility:** When using sandboxing, ensure MCP servers are
  available within the sandbox environment
- **Private data:** Using broadly scoped personal access tokens can lead to
  information leakage between repositories

### Performance and resource management

- **Connection persistence:** The CLI maintains persistent connections to
  servers that successfully register tools
- **Automatic cleanup:** Connections to servers providing no tools are
  automatically closed
- **Timeout management:** Configure appropriate timeouts based on your server's
  response characteristics
- **Resource monitoring:** MCP servers run as separate processes and consume
  system resources

### Schema compatibility

- **Property stripping:** The system automatically removes certain schema
  properties (`$schema`, `additionalProperties`) for Gemini API compatibility
- **Name sanitization:** Tool names are automatically sanitized to meet API
  requirements
- **Conflict resolution:** Tool name conflicts between servers are resolved
  through automatic prefixing

This comprehensive integration makes MCP servers a powerful way to extend the
Gemini CLI's capabilities while maintaining security, reliability, and ease of
use.

## Returning rich content from tools

MCP tools are not limited to returning simple text. You can return rich,
multi-part content, including text, images, audio, and other binary data in a
single tool response. This allows you to build powerful tools that can provide
diverse information to the model in a single turn.

All data returned from the tool is processed and sent to the model as context
for its next generation, enabling it to reason about or summarize the provided
information.

### How it works

To return rich content, your tool's response must adhere to the MCP
specification for a
[`CallToolResult`](https://modelcontextprotocol.io/specification/2025-06-18/server/tools#tool-result).
The `content` field of the result should be an array of `ContentBlock` objects.
The Gemini CLI will correctly process this array, separating text from binary
data and packaging it for the model.

You can mix and match different content block types in the `content` array. The
supported block types include:

- `text`
- `image`
- `audio`
- `resource` (embedded content)
- `resource_link`

### Example: Returning text and an image

Here is an example of a valid JSON response from an MCP tool that returns both a
text description and an image:

```json
{
  "content": [
    {
      "type": "text",
      "text": "Here is the logo you requested."
    },
    {
      "type": "image",
      "data": "BASE64_ENCODED_IMAGE_DATA_HERE",
      "mimeType": "image/png"
    },
    {
      "type": "text",
      "text": "The logo was created in 2025."
    }
  ]
}
```

When the Gemini CLI receives this response, it will:

1.  Extract all the text and combine it into a single `functionResponse` part
    for the model.
2.  Present the image data as a separate `inlineData` part.
3.  Provide a clean, user-friendly summary in the CLI, indicating that both text
    and an image were received.

This enables you to build sophisticated tools that can provide rich, multi-modal
context to the Gemini model.

## MCP prompts as slash commands

In addition to tools, MCP servers can expose predefined prompts that can be
executed as slash commands within the Gemini CLI. This allows you to create
shortcuts for common or complex queries that can be easily invoked by name.

### Defining prompts on the server

Here's a small example of a stdio MCP server that defines prompts:

```ts
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { z } from 'zod';

const server = new McpServer({
  name: 'prompt-server',
  version: '1.0.0',
});

server.registerPrompt(
  'poem-writer',
  {
    title: 'Poem Writer',
    description: 'Write a nice haiku',
    argsSchema: { title: z.string(), mood: z.string().optional() },
  },
  ({ title, mood }) => ({
    messages: [
      {
        role: 'user',
        content: {
          type: 'text',
          text: `Write a haiku${mood ? ` with the mood ${mood}` : ''} called ${title}. Note that a haiku is 5 syllables followed by 7 syllables followed by 5 syllables `,
        },
      },
    ],
  }),
);

const transport = new StdioServerTransport();
await server.connect(transport);
```

This can be included in `settings.json` under `mcpServers` with:

```json
{
  "mcpServers": {
    "nodeServer": {
      "command": "node",
      "args": ["filename.ts"]
    }
  }
}
```

### Invoking prompts

Once a prompt is discovered, you can invoke it using its name as a slash
command. The CLI will automatically handle parsing arguments.

```bash
/poem-writer --title="Gemini CLI" --mood="reverent"
```

or, using positional arguments:

```bash
/poem-writer "Gemini CLI" reverent
```

When you run this command, the Gemini CLI executes the `prompts/get` method on
the MCP server with the provided arguments. The server is responsible for
substituting the arguments into the prompt template and returning the final
prompt text. The CLI then sends this prompt to the model for execution. This
provides a convenient way to automate and share common workflows.

## Managing MCP servers with `gemini mcp`

While you can always configure MCP servers by manually editing your
`settings.json` file, the Gemini CLI provides a convenient set of commands to
manage your server configurations programmatically. These commands streamline
the process of adding, listing, and removing MCP servers without needing to
directly edit JSON files.

### Adding a server (`gemini mcp add`)

The `add` command configures a new MCP server in your `settings.json`. Based on
the scope (`-s, --scope`), it will be added to either the user config
`~/.gemini/settings.json` or the project config `.gemini/settings.json` file.

**Command:**

```bash
gemini mcp add [options] <name> <commandOrUrl> [args...]
```

- `<name>`: A unique name for the server.
- `<commandOrUrl>`: The command to execute (for `stdio`) or the URL (for
  `http`/`sse`).
- `[args...]`: Optional arguments for a `stdio` command.

**Options (flags):**

- `-s, --scope`: Configuration scope (user or project). [default: "project"]
- `-t, --transport`: Transport type (stdio, sse, http). [default: "stdio"]
- `-e, --env`: Set environment variables (e.g. -e KEY=value).
- `-H, --header`: Set HTTP headers for SSE and HTTP transports (e.g. -H
  "X-Api-Key: abc123" -H "Authorization: Bearer abc123").
- `--timeout`: Set connection timeout in milliseconds.
- `--trust`: Trust the server (bypass all tool call confirmation prompts).
- `--description`: Set the description for the server.
- `--include-tools`: A comma-separated list of tools to include.
- `--exclude-tools`: A comma-separated list of tools to exclude.

#### Adding an stdio server

This is the default transport for running local servers.

```bash
# Basic syntax
gemini mcp add [options] <name> <command> [args...]

# Example: Adding a local server
gemini mcp add -e API_KEY=123 -e DEBUG=true my-stdio-server /path/to/server arg1 arg2 arg3

# Example: Adding a local python server
gemini mcp add python-server python server.py -- --server-arg my-value
```

#### Adding an HTTP server

This transport is for servers that use the streamable HTTP transport.

```bash
# Basic syntax
gemini mcp add --transport http <name> <url>

# Example: Adding an HTTP server
gemini mcp add --transport http http-server https://api.example.com/mcp/

# Example: Adding an HTTP server with an authentication header
gemini mcp add --transport http --header "Authorization: Bearer abc123" secure-http https://api.example.com/mcp/
```

#### Adding an SSE server

This transport is for servers that use Server-Sent Events (SSE).

```bash
# Basic syntax
gemini mcp add --transport sse <name> <url>

# Example: Adding an SSE server
gemini mcp add --transport sse sse-server https://api.example.com/sse/

# Example: Adding an SSE server with an authentication header
gemini mcp add --transport sse --header "Authorization: Bearer abc123" secure-sse https://api.example.com/sse/
```

### Listing servers (`gemini mcp list`)

To view all MCP servers currently configured, use the `list` command. It
displays each server's name, configuration details, and connection status. This
command has no flags.

**Command:**

```bash
gemini mcp list
```

**Example output:**

```sh
✓ stdio-server: command: python3 server.py (stdio) - Connected
✓ http-server: https://api.example.com/mcp (http) - Connected
✗ sse-server: https://api.example.com/sse (sse) - Disconnected
```

### Removing a server (`gemini mcp remove`)

To delete a server from your configuration, use the `remove` command with the
server's name.

**Command:**

```bash
gemini mcp remove <name>
```

**Options (flags):**

- `-s, --scope`: Configuration scope (user or project). [default: "project"]

**Example:**

```bash
gemini mcp remove my-server
```

This will find and delete the "my-server" entry from the `mcpServers` object in
the appropriate `settings.json` file based on the scope (`-s, --scope`).

## Instructions

Gemini CLI supports
[MCP server instructions](https://modelcontextprotocol.io/specification/2025-06-18/schema#initializeresult),
which will be appended to the system instructions.
