AI analytics#1339
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- Added new internal API endpoint for documentation tools, allowing actions such as listing available docs, searching, and fetching specific documentation by ID. - Updated environment configuration to support optional internal secret for enhanced security. - Refactored existing search functionality to utilize the new docs tools API instead of the previous MCP server. - Improved error handling and response parsing for documentation-related requests. - Expanded documentation to clarify the relationship between the new tools and existing API functionalities. This update streamlines the documentation access process and enhances the overall developer experience.
- Introduced error capturing for failed HTTP requests in the docs tools API, improving debugging capabilities. - Updated the API response for unsupported methods to include an 'Allow' header, clarifying the expected request type. These changes enhance the robustness of the documentation tools integration and improve developer experience.
- Updated the key name in the capabilities section of the API documentation to follow a consistent naming convention, improving clarity and maintainability.
The .gitmodules was updated in d22593d to point at apps/backend/src/private/implementation, but the gitlink entry (mode 160000) was never added to the tree. This caused `git clone --recurse-submodules` to silently skip the private submodule. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…on for docs tools - Added `STACK_DOCS_INTERNAL_BASE_URL` to backend `.env` and `.env.development` files for AI tool bundle configuration. - Removed references to `STACK_INTERNAL_DOCS_TOOLS_SECRET` from backend and docs environment files and validation logic from the docs tools API route. - Introduced a new `.env` file for the docs app with essential configuration variables.
… into llm-mcp-flow
…tasks and improve serialization robustness
…Dashboard and updateDashboard based on context
… it is a valid integer and within bounds
…r improved clarity and maintainability
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This PR adds an AI analytics dashboard to the internal tool and centralises AI query observability. Every call through the /ai/query and /integrations/ai-proxy endpoints is now logged to SpacetimeDB with token counts, cost, latency, and full conversation replay. The internal tool gains a new "Unified AI Endpoint Analytics" tab backed by a new useAiQueryLogs SpacetimeDB subscription, plus reviewer enrollment and unmark-reviewed flows.
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Summary by CodeRabbit
New Features
Improvements
Infrastructure