Files
AutoGPT/autogpt_platform
Zamil Majdy f9f358c526 feat(mcp): Add MCP tool block with OAuth, tool discovery, and standard credential integration (#12011)
## Summary

<img width="1000" alt="image"
src="https://github.com/user-attachments/assets/18e8ef34-d222-453c-8b0a-1b25ef8cf806"
/>


<img width="250" alt="image"
src="https://github.com/user-attachments/assets/ba97556c-09c5-4f76-9f4e-49a2e8e57468"
/>

<img width="250" alt="image"
src="https://github.com/user-attachments/assets/68f7804a-fe74-442d-9849-39a229c052cf"
/>

<img width="250" alt="image"
src="https://github.com/user-attachments/assets/700690ba-f9fe-4726-8871-3bfbab586001"
/>

Full-stack MCP (Model Context Protocol) tool block integration that
allows users to connect to any MCP server, discover available tools,
authenticate via OAuth, and execute tools — all through the standard
AutoGPT credential system.

### Backend

- **MCPToolBlock** (`blocks/mcp/block.py`): New block using
`CredentialsMetaInput` pattern with optional credentials (`default={}`),
supporting both authenticated (OAuth) and public MCP servers. Includes
auto-lookup fallback for backward compatibility.
- **MCP Client** (`blocks/mcp/client.py`): HTTP transport with JSON-RPC
2.0, tool discovery, tool execution with robust error handling
(type-checked error fields, non-JSON response handling)
- **MCP OAuth Handler** (`blocks/mcp/oauth.py`): RFC 8414 discovery,
dynamic per-server OAuth with PKCE, token storage and refresh via
`raise_for_status=True`
- **MCP API Routes** (`api/features/mcp/routes.py`): `discover-tools`,
`oauth/login`, `oauth/callback` endpoints with credential cleanup,
defensive OAuth metadata validation
- **Credential system integration**:
- `CredentialsMetaInput` model_validator normalizes legacy
`"ProviderName.MCP"` format from Python 3.13's `str(StrEnum)` change
- `CredentialsFieldInfo.combine()` supports URL-based credential
discrimination (each MCP server gets its own credential entry)
- `aggregate_credentials_inputs` checks block schema defaults for
credential optionality
- Executor normalizes credential data for both Pydantic and JSON schema
validation paths
  - Chat credential matching handles MCP server URL filtering
- `provider_matches()` helper used consistently for Python 3.13 StrEnum
compatibility
- **Pre-run validation**: `_validate_graph_get_errors` now calls
`get_missing_input()` for custom block-level validation (MCP tool
arguments)
- **Security**: HTML tag stripping loop to prevent XSS bypass, SSRF
protection (removed trusted_origins)

### Frontend

- **MCPToolDialog** (`MCPToolDialog.tsx`): Full tool discovery UI —
enter server URL, authenticate if needed, browse tools, select tool and
configure
- **OAuth popup** (`oauth-popup.ts`): Shared utility supporting
cross-origin MCP OAuth flows with BroadcastChannel + localStorage
fallback
- **Credential integration**: MCP-specific OAuth flow in
`useCredentialsInput`, server URL filtering in `useCredentials`, MCP
callback page
- **CredentialsSelect**: Auto-selects first available credential instead
of defaulting to "None", credentials listed before "None" in dropdown
- **Node rendering**: Dynamic tool input schema rendering on MCP nodes,
proper handling in both legacy and new flow editors
- **Block title persistence**: `customized_name` set at block creation
for both MCP and Agent blocks — no fallback logic needed, titles survive
save/load reliably
- **Stable credential ordering**: Removed `sortByUnsetFirst` that caused
credential inputs to jump when selected

### Tests (~2060 lines)

- Unit tests: block, client, tool execution
- Integration tests: mock MCP server with auth
- OAuth flow tests
- API endpoint tests
- Credential combining/optionality tests
- E2e tests (skipped in CI, run manually)

## Key Design Decisions

1. **Optional credentials via `default={}`**: MCP servers can be public
(no auth) or private (OAuth). The `credentials` field has `default={}`
making it optional at the schema level, so public servers work without
prompting for credentials.

2. **URL-based credential discrimination**: Each MCP server URL gets its
own credential entry in the "Run agent" form (via
`discriminator="server_url"`), so agents using multiple MCP servers
prompt for each independently.

3. **Model-level normalization**: Python 3.13 changed `str(StrEnum)` to
return `"ClassName.MEMBER"`. Rather than scattering fixes across the
codebase, a Pydantic `model_validator(mode="before")` on
`CredentialsMetaInput` handles normalization centrally, and
`provider_matches()` handles lookups.

4. **Credential auto-select**: `CredentialsSelect` component defaults to
the first available credential and notifies the parent state, ensuring
credentials are pre-filled in the "Run agent" dialog without requiring
manual selection.

5. **customized_name for block titles**: Both MCP and Agent blocks set
`customized_name` in metadata at creation time. This eliminates
convoluted runtime fallback logic (`agent_name`, hostname extraction) —
the title is persisted once and read directly.

## Test plan

- [x] Unit/integration tests pass (68 MCP + 11 graph = 79 tests)
- [x] Manual: MCP block with public server (DeepWiki) — no credentials
needed, tools discovered and executable
- [x] Manual: MCP block with OAuth server (Linear, Sentry) — OAuth flow
prompts correctly
- [x] Manual: "Run agent" form shows correct credential requirements per
MCP server
- [x] Manual: Credential auto-selects when exactly one matches,
pre-selects first when multiple exist
- [x] Manual: Credential ordering stays stable when
selecting/deselecting
- [x] Manual: MCP block title persists after save and refresh
- [x] Manual: Agent block title persists after save and refresh (via
customized_name)
- [ ] Manual: Shared agent with MCP block prompts new user for
credentials

---------

Co-authored-by: Otto <otto@agpt.co>
Co-authored-by: Ubbe <hi@ubbe.dev>
2026-02-13 16:17:03 +00:00
..

AutoGPT Platform

Welcome to the AutoGPT Platform - a powerful system for creating and running AI agents to solve business problems. This platform enables you to harness the power of artificial intelligence to automate tasks, analyze data, and generate insights for your organization.

Getting Started

Prerequisites

  • Docker
  • Docker Compose V2 (comes with Docker Desktop, or can be installed separately)

Running the System

To run the AutoGPT Platform, follow these steps:

  1. Clone this repository to your local machine and navigate to the autogpt_platform directory within the repository:

    git clone <https://github.com/Significant-Gravitas/AutoGPT.git | git@github.com:Significant-Gravitas/AutoGPT.git>
    cd AutoGPT/autogpt_platform
    
  2. Run the following command:

    cp .env.default .env
    

    This command will copy the .env.default file to .env. You can modify the .env file to add your own environment variables.

  3. Run the following command:

    docker compose up -d
    

    This command will start all the necessary backend services defined in the docker-compose.yml file in detached mode.

  4. After all the services are in ready state, open your browser and navigate to http://localhost:3000 to access the AutoGPT Platform frontend.

Running Just Core services

You can now run the following to enable just the core services.

# For help
make help

# Run just Supabase + Redis + RabbitMQ
make start-core

# Stop core services
make stop-core

# View logs from core services 
make logs-core

# Run formatting and linting for backend and frontend
make format

# Run migrations for backend database
make migrate

# Run backend server
make run-backend

# Run frontend development server
make run-frontend

Docker Compose Commands

Here are some useful Docker Compose commands for managing your AutoGPT Platform:

  • docker compose up -d: Start the services in detached mode.
  • docker compose stop: Stop the running services without removing them.
  • docker compose rm: Remove stopped service containers.
  • docker compose build: Build or rebuild services.
  • docker compose down: Stop and remove containers, networks, and volumes.
  • docker compose watch: Watch for changes in your services and automatically update them.

Sample Scenarios

Here are some common scenarios where you might use multiple Docker Compose commands:

  1. Updating and restarting a specific service:

    docker compose build api_srv
    docker compose up -d --no-deps api_srv
    

    This rebuilds the api_srv service and restarts it without affecting other services.

  2. Viewing logs for troubleshooting:

    docker compose logs -f api_srv ws_srv
    

    This shows and follows the logs for both api_srv and ws_srv services.

  3. Scaling a service for increased load:

    docker compose up -d --scale executor=3
    

    This scales the executor service to 3 instances to handle increased load.

  4. Stopping the entire system for maintenance:

    docker compose stop
    docker compose rm -f
    docker compose pull
    docker compose up -d
    

    This stops all services, removes containers, pulls the latest images, and restarts the system.

  5. Developing with live updates:

    docker compose watch
    

    This watches for changes in your code and automatically updates the relevant services.

  6. Checking the status of services:

    docker compose ps
    

    This shows the current status of all services defined in your docker-compose.yml file.

These scenarios demonstrate how to use Docker Compose commands in combination to manage your AutoGPT Platform effectively.

Persisting Data

To persist data for PostgreSQL and Redis, you can modify the docker-compose.yml file to add volumes. Here's how:

  1. Open the docker-compose.yml file in a text editor.

  2. Add volume configurations for PostgreSQL and Redis services:

    services:
      postgres:
        # ... other configurations ...
        volumes:
          - postgres_data:/var/lib/postgresql/data
    
      redis:
        # ... other configurations ...
        volumes:
          - redis_data:/data
    
    volumes:
      postgres_data:
      redis_data:
    
  3. Save the file and run docker compose up -d to apply the changes.

This configuration will create named volumes for PostgreSQL and Redis, ensuring that your data persists across container restarts.

API Client Generation

The platform includes scripts for generating and managing the API client:

  • pnpm fetch:openapi: Fetches the OpenAPI specification from the backend service (requires backend to be running on port 8006)
  • pnpm generate:api-client: Generates the TypeScript API client from the OpenAPI specification using Orval
  • pnpm generate:api: Runs both fetch and generate commands in sequence

Manual API Client Updates

If you need to update the API client after making changes to the backend API:

  1. Ensure the backend services are running:

    docker compose up -d
    
  2. Generate the updated API client:

    pnpm generate:api
    

This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.