DocuMindDocuMindReal test notes from running the system: what we executed, what we saw, and what still needs cleanup.
We started from Codex CLI and kicked off a full crawl + ingest flow using a Sevelet documentation link as the source.
What we did: Started a Codex CLI session, shared the Sevelet documentation link, and asked the agent to crawl docs and track everything in VectorDB.
What happened: The run started exactly as expected and moved into setup prompts before ingestion.
Evidence
What we did: Provided the requested instance name and namespace name to initialize the context.
What happened: The system requested context first, then continued execution with a concrete target namespace.
Evidence
The crawler fetched docs, generated markdown pages locally, and pushed them through DCLI ingestion flow.
What we did: Observed the live execution trace while the crawler fetched docs and DCLI handled ingestion.
What happened: The crawler + ingest loop ran continuously over discovered pages.
Evidence
What we did: Reviewed all generated pages stored by the Python crawler script.
What happened: A complete set of crawled pages appeared in the local pages directory.
Evidence
What we did: Inspected the crawler/ingestion script created during the run.
What happened: The script was generated and used to automate repeated fetch-and-ingest behavior.
Evidence
What we did: Added DCLI as a skill and continued ingestion from stored markdown files.
What happened: The agent repeatedly called DocuMind tools and pushed content into VectorDB.
Evidence
We validated ingestion outcome with logs to confirm data was actually stored, not just reported as done.
What we did: Checked ingestion logs after tool calls completed.
What happened: Logs showed successful text storage in VectorDB.
Evidence
What we did: Verified final task completion output and post-run logs.
What happened: Run completed and confirmed end-to-end storage through DCLI.
Evidence
After ingestion, we tested whether the application could actually retrieve grounded answers using DCLI + vector embeddings.
What we did: Ran the app after indexing all docs and tested real questions.
What happened: The system moved from ingestion mode into retrieval + answer generation mode.
Evidence
What we did: Tracked how the agent explored tools and attempted MCP connection for efficient querying.
What happened: The connection flow resolved correctly and tool usage continued.
Evidence
What we did: Validated tool call responses from MCP endpoints.
What happened: MCP calls returned successful responses used by the agent.
Evidence
What we did: Observed behavior when required context was missing.
What happened: The flow created the missing piece and resumed task execution.
Evidence
We verified dual-path reliability: agent falls back to MCP when CLI has issues, and prefers DCLI when available.
What we did: Observed an execution moment where CLI path had issues.
What happened: The agent switched to MCP fallback path and kept the workflow moving.
Evidence
What we did: Ran the same style task where DCLI path was available.
What happened: The agent found tools, listed instances, set active context, and ingested resources cleanly.
Evidence
The test run proved the workflow can crawl docs, transform them into markdown resources, ingest them into VectorDB, and answer queries using grounded tool-based retrieval.
What we did: Reviewed the complete run from initial prompt to final query behavior.
What happened: End-to-end pipeline worked with practical resilience: ingestion, storage verification, retrieval, and fallback execution paths.
Evidence
Each evidence slot is already mapped to your testing timeline. When you add images, set the `image` field for that step in `testingSections` and the panel will auto-render it.
File: documentation/lib/docs-data.ts