LocalAI/docs/content/features/mcp.md

+++
title = "Model Context Protocol (MCP)"
weight = 22
toc = true
description = "Agentic capabilities with Model Context Protocol integration"
tags = ["MCP", "Agents", "Tools", "Advanced"]
categories = ["Features"]
+++

![Server-side vs client-side MCP: the model's tool loop runs on the server or in the browser](/images/diagrams/mcp-server-vs-client.png)


LocalAI now supports the **Model Context Protocol (MCP)**, enabling powerful agentic capabilities by connecting AI models to external tools and services. This feature allows your LocalAI models to interact with various MCP servers, providing access to real-time data, APIs, and specialized tools.

{{< agentic-routing current="mcp" >}}

## What is MCP?

The Model Context Protocol is a standard for connecting AI models to external tools and data sources. It enables AI agents to:

- Access real-time information from external APIs
- Execute commands and interact with external systems
- Use specialized tools for specific tasks
- Maintain context across multiple tool interactions

## Key Features

- **Real-time Tool Access**: Connect to external MCP servers for live data
- **Multiple Server Support**: Configure both remote HTTP and local stdio servers
- **Cached Connections**: Efficient tool caching for better performance
- **Secure Authentication**: Static bearer tokens or OAuth2 `client_credentials` with automatic token refresh
- **Multi-endpoint Support**: Works with OpenAI Chat, Anthropic Messages, and Open Responses APIs
- **Selective Server Activation**: Use `metadata.mcp_servers` to enable only specific servers per request
- **Server-side Tool Execution**: Tools are executed on the server and results fed back to the model automatically
- **Agent Configuration**: Customizable execution limits and retry behavior
- **MCP Prompts**: Discover and expand reusable prompt templates from MCP servers
- **MCP Resources**: Browse and inject resource content (files, data) from MCP servers into conversations

## Configuration

MCP support is configured in your model's YAML configuration file using the `mcp` section:

```yaml
name: my-mcp-model
backend: llama-cpp
parameters:
  model: qwen3-4b.gguf

mcp:
  remote: |
    {
      "mcpServers": {
        "weather-api": {
          "url": "https://api.weather.com/v1",
          "token": "your-api-token"
        },
        "search-engine": {
          "url": "https://search.example.com/mcp",
          "token": "your-search-token"
        }
      }
    }

  stdio: |
    {
      "mcpServers": {
        "file-manager": {
          "command": "python",
          "args": ["-m", "mcp_file_manager"],
          "env": {
            "API_KEY": "your-key"
          }
        },
        "database-tools": {
          "command": "node",
          "args": ["database-mcp-server.js"],
          "env": {
            "DB_URL": "postgresql://localhost/mydb"
          }
        }
      }
    }

agent:
  max_iterations: 10             # Maximum MCP tool execution loop iterations
```

### Configuration Options

In the interactive model editor, **Remote MCP Servers** and **MCP STDIO Servers** are YAML editors. Enter only the embedded `mcpServers` document shown inside the `remote: |` or `stdio: |` block above; the editor supplies the outer model configuration keys.

#### Remote Servers (`remote`)
Configure HTTP-based MCP servers:

- **`url`**: The MCP server endpoint URL
- **`token`**: Static bearer token for authentication (optional)
- **`oauth2`**: OAuth2 `client_credentials` settings (optional, mutually exclusive with `token`); see [OAuth2 client credentials](#oauth2-client-credentials-for-remote-servers)

LocalAI automatically selects the transport for remote model MCP servers. It tries Streamable HTTP first, including servers that do not assign session IDs. If the initial POST returns HTTP 400, 404, or 405, LocalAI retries with legacy SSE. Both attempts share the discovery timeout and use the configured bearer token. Authentication failures and redirects do not trigger fallback.

Use the endpoint URL published by your server: usually `/mcp` for Streamable HTTP or `/sse` for legacy SSE. Legacy SSE servers must advertise a message endpoint on the same origin (scheme, host, and port). No transport setting is required.

Remote model MCP connections originate from the LocalAI process. If LocalAI runs in Docker, the URL must therefore resolve and be reachable **from the LocalAI container**, not only from the host browser. For another service in the same Compose project, use its Compose service name and container port. Host-only DNS names, VPN DNS, and private routes must also be made available inside the container.

#### OAuth2 client credentials for remote servers

When an MCP server sits behind an OpenID Connect / OAuth2 provider (for example authentik, Keycloak, Entra ID or Auth0), LocalAI can obtain short-lived access tokens with the `client_credentials` grant instead of using a long-lived static `token`:

```yaml
mcp:
  remote: |
    {
      "mcpServers": {
        "internal-tools": {
          "url": "https://mcp.example.com/mcp",
          "oauth2": {
            "token_url": "https://auth.example.com/application/o/token/",
            "client_id": "localai",
            "client_secret_env": "MCP_INTERNAL_TOOLS_SECRET",
            "scopes": ["mcp:tools"],
            "endpoint_params": {"audience": "mcp-gateway"}
          }
        }
      }
    }
```

| Field | Description |
|---|---|
| `token_url` | Token endpoint of the identity provider (required). Must use `https`; plain `http` is accepted only for `localhost`, `127.0.0.1` and `::1` |
| `client_id` / `client_id_env` | Client ID, literally or as the name of an environment variable (exactly one is required) |
| `client_secret` / `client_secret_env` | Client secret, literally or as the name of an environment variable (exactly one is required). Use `client_secret_env` instead of a literal `client_secret` so the secret is not stored in the model YAML or the server configuration |
| `scopes` | Scopes to request (optional) |
| `endpoint_params` | Extra form parameters for the token request, e.g. `audience` or `resource` (RFC 8707) (optional) |

Behaviour:

- The token is fetched on the first request to the server, cached, and sent as `Authorization: Bearer <token>` on every request, for both streamable HTTP and SSE transports.
- It is refreshed proactively once **half** of its lifetime (`expires_in`) has elapsed. If the provider omits `expires_in`, a lifetime of 5 minutes is assumed.
- If a refresh fails, the current token keeps being used until 30 seconds before it expires (retries wait at least 5 seconds after a failed fetch, including the first fetch). After that, requests to the MCP server fail with an error; LocalAI never falls back to an unauthenticated request.
- Configuring both `token` and `oauth2` for the same server is rejected when the model configuration is loaded, as are missing or duplicated `client_id`/`client_secret` sources. An environment variable named in `*_env` that is unset or empty makes the connection to that server fail, and the server is reported with an error in the server listing.
- Debug logs of remote server entries redact static tokens and OAuth2 client secrets in both text and JSON formats.
- Token caches and retry timers are local to each connection-owning process. Workers obtain and refresh their own tokens; this cache is not shared across the cluster.
- The environment variables are read by the process that connects to the MCP server. In distributed mode that is the agent worker, so set them there.
- Sessions and cached tokens are keyed by the server configuration, not by the value of an environment variable. After you rotate a secret that is referenced by `client_secret_env` (or `client_id_env`), restart the process that connects to the MCP server so it picks up the new value.

Servers configured with a static `token` behave exactly as before.

#### STDIO Servers (`stdio`)
Configure local command-based MCP servers:

- **`command`**: The executable command to run
- **`args`**: Array of command-line arguments
- **`env`**: Environment variables (optional)

#### Automatic activation (`auto`)

`/v1/chat/completions` only runs a model's MCP servers when the request selects
them through `metadata.mcp_servers`. Plain OpenAI clients cannot send that field.
Set `auto: true` in the `mcp:` block to enable every configured server when the
request names none:

```yaml
mcp:
  auto: true
  remote: |
    {"mcpServers": {"weather-api": {"url": "https://weather.example.com/mcp"}}}
```

A request that does send `metadata.mcp_servers` still gets exactly the servers it
names. `/v1/responses` already enables all servers when the request sends no
`tools`, and `/mcp/v1/chat/completions` always does; `auto` does not change them.

`auto` only applies when MCP is enabled on the instance (not disabled with
`LOCALAI_DISABLE_MCP`) and, with authentication on, the caller has the `mcp`
permission. Otherwise the request is answered as a plain chat: no server is
contacted and no tools are added. It is not rejected, because the client did not
ask for MCP.

#### Client tools (`client_tools`)

`client_tools: drop` (a top-level model config key) discards the `tools`,
`functions` and `tool_choice` a client sends, so the model is offered only its own
MCP tools. Use it when a front end attaches tools of its own that should not
reach this model. Empty (the default) keeps client tools.

On a [model alias]({{%relref "features/model-aliases" %}}#profile-aliases) these
keys go inside the alias's `profile` block, which lets several assistants with
different tools share one loaded model.

#### Agent Configuration (`agent`)

- **`max_iterations`**: Maximum number of MCP tool execution loop iterations (default: 10). Each iteration allows the model to call tools and receive results before generating the next response.

## Agent-scoped MCP servers

The `mcp:` block above attaches MCP servers to a **model**: every request that uses that model config can reach those tools. LocalAI's [agents]({{% relref "features/agents" %}}) can also attach MCP servers to a single **agent**, independent of the model it runs on. This is the mechanism the Agents feature advertises as "MCP Servers".

Use model-scoped MCP (the `mcp:` block in a model YAML) when the tools belong to the model itself and every caller of that model should get them. Use agent-scoped MCP when only one agent should have a given tool, or when different agents sharing the same model need different tools.

Agent-scoped MCP servers are configured per agent, not in a model YAML:

- **In the web UI:** open the **Agents** page, edit an agent, and fill in its **MCP Servers** (remote HTTP servers) and **MCP STDIO Servers** (local command servers) fields. The JSON shape is the same `mcpServers` map used above.
- **In an agent config (JSON):** the agent stores remote servers under `mcp_servers` and local command servers under `mcp_stdio_servers`, so an exported/imported agent carries its MCP servers with it.

An agent-scoped MCP server that is unreachable is a common reason an imported agent's tool "times out or is unavailable"; see the checklist in {{% relref "getting-started/first-agent" %}}.

## Usage

### Selecting MCP Servers via `metadata`

All API endpoints support MCP server selection through the standard `metadata` field. Pass a comma-separated list of server names in `metadata.mcp_servers`:

- **When present**: Only the named MCP servers are activated for this request. Server names must match the keys in the model's MCP config YAML (e.g., `"weather-api"`, `"search-engine"`).
- **When absent**: Behavior depends on the endpoint:
  - **OpenAI Chat Completions** and **Anthropic Messages**: No MCP tools are injected (standard behavior).
  - **Open Responses**: If the model has MCP config and no user-provided tools, all MCP servers are auto-activated (backward compatible).

The `mcp_servers` metadata key is consumed by the MCP engine and stripped before reaching the backend. Clients that support the standard `metadata` field can use this without custom schema extensions.

### API Endpoints

MCP tools work across all three API endpoints:

#### OpenAI Chat Completions (`/v1/chat/completions`)

```bash
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "my-mcp-model",
    "messages": [{"role": "user", "content": "What is the weather in New York?"}],
    "metadata": {"mcp_servers": "weather-api"},
    "stream": true
  }'
```

#### Anthropic Messages (`/v1/messages`)

```bash
curl http://localhost:8080/v1/messages \
  -H "Content-Type: application/json" \
  -d '{
    "model": "my-mcp-model",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "What is the weather in New York?"}],
    "metadata": {"mcp_servers": "weather-api"}
  }'
```

#### Open Responses (`/v1/responses`)

```bash
curl http://localhost:8080/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "my-mcp-model",
    "input": "What is the weather in New York?",
    "metadata": {"mcp_servers": "weather-api"}
  }'
```

The tools LocalAI runs server-side are part of the response: `output` lists a
`function_call` item and its `function_call_output` item for every MCP tool call,
in the order they ran, before the final `message`. Replayed tool results retain
the `call_id` association with their calls. If the streaming iteration limit is
reached, each executed tool round appears once in the final output. This holds for streaming
(`response.completed`) and non-streaming requests and for the stored response.
A follow-up request with `previous_response_id` therefore replays the earlier
tool calls and results to the model, and a client that keeps the `output` items
and sends them back as input does the same. Without this trail, models tend to
answer follow-up questions from the earlier text instead of calling the tool
again. Chat completions (`/v1/chat/completions`) return only the final answer,
so a client cannot carry the tool trail over to the next turn there.

### Server Listing Endpoint

You can list available MCP servers and their tools for a given model:

```bash
curl http://localhost:8080/v1/mcp/servers/my-mcp-model
```

Returns a model wrapper and one status entry for every configured server:

```json
{
  "model": "my-mcp-model",
  "servers": [
    {
      "name": "weather-api",
      "type": "remote",
      "tools": ["get_weather", "get_forecast"]
    },
    {
      "name": "search-engine",
      "type": "remote",
      "tools": [],
      "error": "connection failed: ..."
    }
  ]
}
```

The Chat **MCP → Servers** tab uses this endpoint. A configured server remains listed when it is unavailable, shows the discovery error, and cannot be selected until a later discovery attempt succeeds. Reopening the dropdown or returning to the Servers tab retries discovery.

When authentication is enabled, the Servers tab is offered to admins and to every user holding the `mcp` feature (on by default for new users). Chat learns that a model has MCP servers from the `mcp` flag of `GET /api/models/capabilities`, so non-admin users do not need access to the model configuration. With `LOCALAI_DISABLE_MCP=true` the flag is never set.

### MCP Prompts

MCP servers can provide reusable prompt templates. LocalAI supports discovering and expanding prompts from MCP servers.

#### List Prompts

```bash
curl http://localhost:8080/v1/mcp/prompts/my-mcp-model
```

Returns:

```json
[
  {
    "name": "code-review",
    "description": "Review code for best practices",
    "title": "Code Review",
    "arguments": [
      {"name": "language", "description": "Programming language", "required": true}
    ],
    "server": "dev-tools"
  }
]
```

#### Expand a Prompt

```bash
curl -X POST http://localhost:8080/v1/mcp/prompts/my-mcp-model/code-review \
  -H "Content-Type: application/json" \
  -d '{"arguments": {"language": "go"}}'
```

Returns:

```json
{
  "messages": [
    {"role": "user", "content": "Please review the following Go code for best practices..."}
  ]
}
```

#### Inject Prompts via Metadata

You can inject MCP prompts into any chat request using `metadata.mcp_prompt` and `metadata.mcp_prompt_args`:

```bash
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "my-mcp-model",
    "messages": [{"role": "user", "content": "Review this function: func add(a, b int) int { return a + b }"}],
    "metadata": {
      "mcp_servers": "dev-tools",
      "mcp_prompt": "code-review",
      "mcp_prompt_args": "{\"language\": \"go\"}"
    }
  }'
```

The prompt messages are prepended to the conversation before inference.

### MCP Resources

MCP servers can expose data/content (files, database records, etc.) as resources identified by URI.

#### List Resources

```bash
curl http://localhost:8080/v1/mcp/resources/my-mcp-model
```

Returns:

```json
[
  {
    "name": "project-readme",
    "uri": "file:///README.md",
    "description": "Project documentation",
    "mimeType": "text/markdown",
    "server": "file-manager"
  }
]
```

#### Read a Resource

```bash
curl -X POST http://localhost:8080/v1/mcp/resources/my-mcp-model/read \
  -H "Content-Type: application/json" \
  -d '{"uri": "file:///README.md"}'
```

Returns:

```json
{
  "uri": "file:///README.md",
  "content": "# My Project\n...",
  "mimeType": "text/markdown"
}
```

#### Inject Resources via Metadata

You can inject MCP resources into chat requests using `metadata.mcp_resources` (comma-separated URIs):

```bash
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "my-mcp-model",
    "messages": [{"role": "user", "content": "Summarize this project"}],
    "metadata": {
      "mcp_servers": "file-manager",
      "mcp_resources": "file:///README.md,file:///CHANGELOG.md"
    }
  }'
```

Resource contents are appended to the last user message as text blocks (following the same approach as llama.cpp's WebUI).

### Legacy Endpoint

The `/mcp/v1/chat/completions` endpoint is still supported for backward compatibility. It automatically enables all configured MCP servers (equivalent to not specifying `mcp_servers`).

```bash
curl http://localhost:8080/mcp/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "my-mcp-model",
    "messages": [
      {"role": "user", "content": "What is the current weather in New York?"}
    ]
  }'
```

### Example Response

```json
{
  "id": "chatcmpl-123",
  "created": 1699123456,
  "model": "my-mcp-model",
  "choices": [
    {
      "text": "The current weather in New York is 72°F (22°C) with partly cloudy skies."
    }
  ],
  "object": "text_completion"
}
```

## Example Configurations

### Docker-based Tools

```yaml
name: docker-agent
backend: llama-cpp
parameters:
  model: qwen3-4b.gguf

mcp:
  stdio: |
    {
      "mcpServers": {
        "searxng": {
          "command": "docker",
          "args": [
            "run", "-i", "--rm",
            "quay.io/mudler/tests:duckduckgo-localai"
          ]
        }
      }
    }

agent:
  max_iterations: 10
```

## How It Works

1. **Tool Discovery**: LocalAI connects to configured MCP servers and discovers available tools
2. **Tool Injection**: Discovered tools are injected into the model's tool/function list alongside any user-provided tools
3. **Inference Loop**: The model generates a response. If it calls MCP tools, LocalAI executes them server-side, appends results to the conversation, and re-runs inference
4. **Response Generation**: When the model produces a final response (no more MCP tool calls), it is returned to the client

The execution loop is bounded by `agent.max_iterations` (default 10) to prevent infinite loops.

## Session Lifecycle

MCP sessions are automatically managed by LocalAI:

- **Lazy initialization**: Sessions are created the first time a model's MCP tools are used
- **Cached per model**: Sessions are reused across requests for the same model
- **Cleanup on model unload**: When a model is unloaded (idle watchdog eviction, manual stop, or shutdown), all associated MCP sessions are closed and resources freed
- **Graceful shutdown**: All MCP sessions are closed when LocalAI shuts down

This means you don't need to manually manage MCP connections - they follow the model's lifecycle automatically.

## Supported MCP Servers

LocalAI is compatible with any MCP-compliant server.

## Best Practices

### Security
- Use environment variables for sensitive tokens
- Validate MCP server endpoints before deployment
- Implement proper authentication for remote servers

### Performance
- Cache frequently used tools
- Use appropriate timeout values for external APIs
- Monitor resource usage for stdio servers

### Error Handling
- Implement fallback mechanisms for tool failures
- Log tool execution for debugging
- Handle network timeouts gracefully

### With External Applications

Use MCP-enabled models in your applications:

```python
import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="your-api-key"
)

response = client.chat.completions.create(
    model="my-mcp-model",
    messages=[
        {"role": "user", "content": "Analyze the latest research papers on AI"}
    ],
    extra_body={"metadata": {"mcp_servers": "search-engine"}}
)
```

### MCP and adding packages

It might be handy to install packages before starting the container to setup the environment. This is an example on how you can do that with docker-compose (installing and configuring docker)

```yaml
services:
  local-ai:
    image: localai/localai:latest
    #image: localai/localai:latest-gpu-nvidia-cuda-13
    #image: localai/localai:latest-gpu-nvidia-cuda-12
    container_name: local-ai
    restart: always
    entrypoint: [ "/bin/bash" ]
    command: >
     -c "apt-get update &&
         apt-get install -y docker.io &&
         /entrypoint.sh"
    environment:
      - DEBUG=true
      - LOCALAI_WATCHDOG_IDLE=true
      - LOCALAI_WATCHDOG_BUSY=true
      - LOCALAI_WATCHDOG_IDLE_TIMEOUT=15m
      - LOCALAI_WATCHDOG_BUSY_TIMEOUT=15m
      - LOCALAI_API_KEY=my-beautiful-api-key
      - DOCKER_HOST=tcp://docker:2376
      - DOCKER_TLS_VERIFY=1
      - DOCKER_CERT_PATH=/certs/client
    ports:
      - "8080:8080"
    volumes:
      - /data/models:/models
      - /data/backends:/backends
      - certs:/certs:ro
    # uncomment for nvidia
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - capabilities: [gpu]
    #           device_ids: ['7']
    # runtime: nvidia

  docker:
    image: docker:dind
    privileged: true
    container_name: docker
    volumes:
      - certs:/certs
    healthcheck:
      test: ["CMD", "docker", "info"]
      interval: 10s
      timeout: 5s
volumes:
  certs:
```

An example model config (to append to any existing model you have) can be:

```yaml
mcp:
  stdio: |
     {
      "mcpServers": {
        "weather": {
          "command": "docker",
          "args": [
            "run", "-i", "--rm",
            "ghcr.io/mudler/mcps/weather:master"
          ]
        },
        "memory": {
          "command": "docker",
          "env": {
            "MEMORY_INDEX_PATH": "/data/memory.bleve"
          },
          "args": [
            "run", "-i", "--rm", "-v", "/host/data:/data",
            "ghcr.io/mudler/mcps/memory:master"
          ]
        },
        "ddg": {
          "command": "docker",
          "env": {
            "MAX_RESULTS": "10"
          },
          "args": [
            "run", "-i", "--rm", "-e", "MAX_RESULTS",
            "ghcr.io/mudler/mcps/duckduckgo:master"
          ]
        }
      }
     }
```

### Links

- [Awesome MCPs](https://github.com/punkpeye/awesome-mcp-servers)
- [A list of MCPs by mudler](https://github.com/mudler/MCPs)

## Client-Side MCP (Browser)

In addition to server-side MCP (where the backend connects to MCP servers), LocalAI supports **client-side MCP** where the browser connects directly to MCP servers. This is inspired by llama.cpp's WebUI and works alongside server-side MCP.

### How It Works

1. **Add servers in the UI**: Click the **MCP** chip above the message box, open the **Client** tab, and add MCP server URLs
2. **Browser connects directly**: The browser uses the MCP TypeScript SDK (`StreamableHTTPClientTransport` or `SSEClientTransport`) to connect to MCP servers
3. **Tool discovery**: Connected servers' tools are sent as `tools` in the chat request body
4. **Browser-side execution**: When the LLM calls a client-side tool, the browser executes it against the MCP server and sends the result back in a follow-up request
5. **Agentic loop**: This continues (up to 10 turns) until the LLM produces a final response

### CORS Proxy

Since browsers enforce CORS restrictions, LocalAI provides a built-in proxy at `/api/cors-proxy`. When "Use CORS proxy" is enabled (default), requests to external MCP servers are routed through:

```
/api/cors-proxy?url=https://remote-mcp-server.example.com/sse
```

The proxy forwards the request method, headers, and body to the target URL and streams the response back with appropriate CORS headers.

### MCP Apps (Interactive Tool UIs)

LocalAI supports the [MCP Apps extension](https://modelcontextprotocol.io/extensions/apps/overview), which allows MCP tools to declare interactive HTML UIs. When a tool has `_meta.ui.resourceUri` in its definition, calling that tool renders the app's HTML inline in the chat as a sandboxed iframe.

**How it works:**

- When the LLM calls a tool with `_meta.ui.resourceUri`, the browser fetches the HTML resource from the MCP server and renders it in an iframe
- The iframe is sandboxed (`allow-scripts allow-forms`, no `allow-same-origin`) for security
- The app can call server tools, send messages, and update context via the `AppBridge` protocol (JSON-RPC over `postMessage`)
- Tools marked as app-only (`_meta.ui.visibility: "app-only"`) are hidden from the LLM and only callable by the app iframe
- On page reload, apps render statically until the MCP connection is re-established

**Requirements:**

- Only works with **client-side MCP** connections (the browser must be connected to the MCP server)
- The MCP server must implement the Apps extension (`_meta.ui.resourceUri` on tools, resource serving)

### Coexistence with Server-Side MCP

Both modes work simultaneously in the same chat:

- **Server-side MCP tools** are configured in model YAML files and executed by the backend. The backend handles these in its own agentic loop.
- **Client-side MCP tools** are configured per-user in the browser and sent as `tools` in the request. When the LLM calls them, the browser executes them.

If both sides have a tool with the same name, the server-side tool takes priority.

### Security Considerations

- The CORS proxy can forward requests to any HTTP/HTTPS URL. It is only available when MCP is enabled (`LOCALAI_DISABLE_MCP` is not set).
- Client-side MCP server configurations are stored in the browser's localStorage and are not shared with the server.
- Custom headers (e.g., API keys) for MCP servers are stored in localStorage. Use with caution on shared machines.

## Disabling MCP Support

You can completely disable MCP functionality in LocalAI by setting the `LOCALAI_DISABLE_MCP` environment variable to `true`, `1`, or `yes`:

```bash
export LOCALAI_DISABLE_MCP=true
```

When this environment variable is set, all MCP-related features will be disabled, including:
- MCP server connections (both remote and stdio)
- Agent tool execution
- The `/mcp/v1/chat/completions` endpoint

This is useful when you want to:
- Run LocalAI without MCP capabilities for security reasons
- Reduce the attack surface by disabling unnecessary features
- Troubleshoot MCP-related issues

### Example

```bash
# Disable MCP completely
LOCALAI_DISABLE_MCP=true localai run

# Or in Docker
docker run -e LOCALAI_DISABLE_MCP=true localai/localai:latest
```

When MCP is disabled, any model configuration with `mcp` sections will be ignored, and attempts to use the MCP endpoint will return an error indicating that MCP support is disabled.