## 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>
This is the frontend for AutoGPT's next generation
🧢 Getting Started
This project uses pnpm as the package manager via corepack. Corepack is a Node.js tool that automatically manages package managers without requiring global installations.
For architecture, conventions, data fetching, feature flags, design system usage, state management, and PR process, see CONTRIBUTING.md. For Playwright and Storybook testing setup, see TESTING.md.
Prerequisites
Make sure you have Node.js 16.10+ installed. Corepack is included with Node.js by default.
Setup
1. Enable corepack (run this once on your system):
corepack enable
This enables corepack to automatically manage pnpm based on the packageManager field in package.json.
2. Install dependencies:
pnpm i
3. Start the development server:
Running the Front-end & Back-end separately
We recommend this approach if you are doing active development on the project. First spin up the Back-end:
# on `autogpt_platform`
docker compose --profile local up deps_backend -d
# on `autogpt_platform/backend`
poetry run app
Then start the Front-end:
# on `autogpt_platform/frontend`
pnpm dev
Open http://localhost:3000 with your browser to see the result. If the server starts on http://localhost:3001 it means the Front-end is already running via Docker. You have to kill the container then or do docker compose down.
You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.
Running both the Front-end and Back-end via Docker
If you run:
# on `autogpt_platform`
docker compose up -d
It will spin up the Back-end and Front-end via Docker. The Front-end will start on port 3000. This might not be
what you want when actively contributing to the Front-end as you won't have direct/easy access to the Next.js dev server.
Subsequent Runs
For subsequent development sessions, you only need to run:
pnpm dev
Every time a new Front-end dependency is added by you or others, you will need to run pnpm i to install the new dependencies.
Available Scripts
pnpm dev- Start development serverpnpm build- Build for productionpnpm start- Start production serverpnpm lint- Run ESLint and Prettier checkspnpm format- Format code with Prettierpnpm types- Run TypeScript type checkingpnpm test- Run Playwright testspnpm test-ui- Run Playwright tests with UIpnpm fetch:openapi- Fetch OpenAPI spec from backendpnpm generate:api-client- Generate API client from OpenAPI specpnpm generate:api- Fetch OpenAPI spec and generate API client
This project uses next/font to automatically optimize and load Inter, a custom Google Font.
🔄 Data Fetching
See CONTRIBUTING.md for guidance on generated API hooks, SSR + hydration patterns, and usage examples. You generally do not need to run OpenAPI commands unless adding/modifying backend endpoints.
🚩 Feature Flags
See CONTRIBUTING.md for feature flag usage patterns, local development with mocks, and how to add new flags.
🚚 Deploy
TODO
📙 Storybook
Storybook is a powerful development environment for UI components. It allows you to build UI components in isolation, making it easier to develop, test, and document your components independently from your main application.
Purpose in the Development Process
- Component Development: Develop and test UI components in isolation.
- Visual Testing: Easily spot visual regressions.
- Documentation: Automatically document components and their props.
- Collaboration: Share components with your team or stakeholders for feedback.
How to Use Storybook
-
Start Storybook: Run the following command to start the Storybook development server:
pnpm storybookThis will start Storybook on port 6006. Open http://localhost:6006 in your browser to view your component library.
-
Build Storybook: To build a static version of Storybook for deployment, use:
pnpm build-storybook -
Running Storybook Tests: Storybook tests can be run using:
pnpm test-storybook -
Writing Stories: Create
.stories.tsxfiles alongside your components to define different states and variations of your components.
By integrating Storybook into our development workflow, we can streamline UI development, improve component reusability, and maintain a consistent design system across the project.
🔭 Tech Stack
Core Framework & Language
- Next.js - React framework with App Router
- React - UI library for building user interfaces
- TypeScript - Typed JavaScript for better developer experience
Styling & UI Components
- Tailwind CSS - Utility-first CSS framework
- shadcn/ui - Re-usable components built with Radix UI and Tailwind CSS
- Radix UI - Headless UI components for accessibility
- Phosphor Icons - Icon set used across the app
- Framer Motion - Animation library for React
Development & Testing
- Storybook - Component development environment
- Playwright - End-to-end testing framework
- ESLint - JavaScript/TypeScript linting
- Prettier - Code formatting
Backend & Services
- Supabase - Backend-as-a-Service (database, auth, storage)
- Sentry - Error monitoring and performance tracking
Package Management
Additional Libraries
- React Hook Form - Forms with easy validation
- Zod - TypeScript-first schema validation
- React Table - Headless table library
- React Flow - Interactive node-based diagrams
- React Query - Data fetching and caching
- React Query DevTools - Debugging tool for React Query
Development Tools
NEXT_PUBLIC_REACT_QUERY_DEVTOOL- Enable React Query DevTools. Set totrueto enable.