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Call the tool coroutine directly instead of ainvoke() to preserve the (content, artifact) tuple that langchain-mcp-adapters returns. Extract structured_content from the artifact dict and propagate it through execute_tool_call (now a 4-tuple) and _execute_single_tool_call via ToolMessage additional_kwargs. Co-authored-by: Cursor <cursoragent@cursor.com>
Introduce ols.src.mcp.tool_registry that discovers and caches UI metadata (_meta.ui.resourceUri, _meta.ui.visibility) from configured MCP servers. The registry maps tool names to ToolUIMetadata and resource URIs to config server names, enabling: - is_model_visible(): filter app-only tools from LLM binding - get_tool_ui_metadata(): look up UI resource URIs for tool results - get_config_name_for_resource_uri(): route ui:// URIs to servers Co-authored-by: Cursor <cursoragent@cursor.com>
Add a FastAPI lifespan handler that calls discover_tool_ui_metadata() during application startup. This populates the tool registry with UI metadata from all configured MCP servers so that tool results can be routed to interactive UIs later. Co-authored-by: Cursor <cursoragent@cursor.com>
Use is_model_visible() to exclude app-only tools (visibility: ["app"]) from LLM tool binding. For tools with a UI resource, send a short placeholder message to the LLM instead of the full tool output to conserve tokens -- the console renders the full data via the interactive UI. Also forward structured_content from tool artifacts in streamed tool_result chunks, handle missing 'properties' key in tool schemas safely, and include tool_name + server_name in result metadata. Co-authored-by: Cursor <cursoragent@cursor.com>
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@onmete: This pull request references OLS-2568 which is a valid jira issue. DetailsIn response to this:
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