Starlette's Route wraps async functions in request_response(), calling
handler(request) instead of handler(scope, receive, send). This broke
the MCP SSE endpoint which needs raw ASGI access. Fix: use a callable
class instead of an async function — Route passes class instances
through as raw ASGI apps without wrapping.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace @app.get() with starlette.routing.Route() for the SSE handler.
The MCP SDK's SseServerTransport calls raw ASGI (scope, receive, send)
internally, which conflicts with Starlette's middleware wrapping.
Also update CONTRIBUTORS.md for PR #1829.
The httpx.AsyncClient() default 5s timeout causes TimeoutException on
slow LLM-backed endpoints. The exception bypasses the HTTPStatusError
handler, propagating as an unhandled error to the MCP framework.
- Add `timeout` parameter to `attach_mcp()` (default None = no limit)
- Pass timeout through to `_make_http_proxy()` and `httpx.AsyncClient()`
- Catch `httpx.TimeoutException` and surface it as HTTP 504
Fixes#1769https://claude.ai/code/session_01LpranMwFBtQU7kFrV5EHAB
This commit introduces several significant enhancements to the Crawl4AI Docker deployment:
1. Add MCP Protocol Support:
- Implement WebSocket and SSE transport layers for MCP server communication
- Create mcp_bridge.py to expose existing API endpoints via MCP protocol
- Add comprehensive tests for both socket and SSE transport methods
2. Enhance Docker Server Capabilities:
- Add PDF generation endpoint with file saving functionality
- Add screenshot capture endpoint with configurable wait time
- Implement JavaScript execution endpoint for dynamic page interaction
- Add intelligent file path handling for saving generated assets
3. Improve Search and Context Functionality:
- Implement syntax-aware code function chunking using AST parsing
- Add BM25-based intelligent document search with relevance scoring
- Create separate code and documentation context endpoints
- Enhance response format with structured results and scores
4. Rename and Fix File Organization:
- Fix typo in test_docker_config_gen.py filename
- Update import statements and dependencies
- Add FileResponse for context endpoints
This enhancement significantly improves the machine-to-machine communication
capabilities of Crawl4AI, making it more suitable for integration with LLM agents
and other automated systems.
The CHANGELOG update has been applied successfully, highlighting the key features and improvements made in this release. The commit message provides a detailed explanation of all the
changes, which will be helpful for tracking the project's evolution.
