7.3 KiB
name, description
| name | description |
|---|---|
| kb-ingest | Process explicitly selected sources for a GEWU project into evidence-bound MCP proposals, with support assessments, coverage and resumable receipts. |
Ingest selected sources
Read the pack's evidence, scope and contribution protocols, then use the ingestion pipeline.
Inputs are the selected source(s), explicit project and the user's research objective. Use get_project_context for the project's north star, key questions, watch list, taxonomy extensions, research lenses and saved ingest guidance. Every project defaults to four research lenses: technical, conceptual, cultural-historical and financial-market. Treat these as coverage prompts, not quotas. Verify the supported tool/schema contract, including ingest_review_create when borderline-review intake is available. Use the connector for local byte transfer; it returns a typed source metadata candidate without exposing the selected path or bytes. Selected .vtt and .srt files produce timestamped transcript cues; cite exact cue start/end times and the quote span. A declared NotebookLM export label is metadata only and is not export verification. For text explicitly pasted by the user, use source_capture_text with a stable capture ID and source key; it accepts UTF-8 plain text or Markdown up to 1 MB, archives and extracts it, and returns a typed candidate marked capture_method=pasted-text. Retry uncertain calls with the same capture ID and identical inputs; changed input under that ID conflicts. It does not register the candidate. source_capture_url fetches only operator-allowlisted public hosts and stages supported text, Markdown, HTML or PDF bytes; it also returns a source metadata candidate but does not register it. Add each new source's exact candidate as register_source in the proposal that cites it, or verify the source was already registered and readable. Use the candidate's revision_id and capture's extraction_id; never invent or substitute these IDs. Submit candidates with the normal typed proposal flow, and use capture-failed candidates only as bookmarks, never as factual evidence. URL source IDs follow the sanitized URL; local source IDs follow the connector's configured file ID; pasted-text source IDs are derived from the supplied stable key and revision IDs include the content digest. PDF extraction currently requires Linux bubblewrap, Poppler and prlimit. Unsupported formats and unavailable parsers must be reported without inventing an extraction. If the service is unavailable, report the blocked stage and only prepare an unaccepted local draft when that work was requested.
Extract useful atomic assertions, problems, techniques or cases with their conditions across all four lenses where the source contains relevant material. Consider cultural history, philosophical context and market/financial evidence as project data when they explain the project's origins, concepts, adoption, audience, costs or viability; they are not automatically peripheral. First try to connect uncertain material to the north star, a key question, a saved rule or one of the lenses. Exclude only clearly unrelated material, unavailable evidence, or material the user explicitly excluded. Do not defer a section merely because its relevance is indirect, non-technical or historical. Continue normal evidence-backed proposal evaluation/submission for all plausible useful findings. When a human should review relevance or categorization, submit a bounded ingest_review_create flag with the exact extraction excerpt/locator, lens, summary and reason before finalizing the associated proposal facets. This persists for notification/filtering and never blocks the evidence-backed proposal flow.
Before building each proposal, use the principal ID assigned to the configured agent credential for proposer_id, node agent_id and assessment assessor_id; do not use the model or harness name. Finish each claim and its evidence before computing assertion_digest or evidence_digests. Evidence assertion paths must point to their exact claim field. Recompute the evidence and assessment digests after any claim/evidence edit, then recompute payload_digest after any operation edit. Follow the exact canonical digest rules in CONTRIBUTIONS.md and EVIDENCE.md. If the identity or digest inputs are unknown, stop and report the missing information rather than guessing.
For tags, audience, use_for and domain, inspect the facet_labels catalog returned by get_project_context. Reuse a canonical project label when its meaning fits; do not create spelling, phrasing or singular/plural variants. Propose only useful, evidence-backed labels, with at most five tags and three labels in each other facet (14 total). Prefer candidate_facet_labels entries shaped as {facet, value, definition} so a genuinely new concept has a concise, distinguishing definition (500 characters max). Legacy candidate_tags, candidate_audience, candidate_use_for and candidate_domain fields remain valid for labels that do not need a definition. Leave uncertain labels out. Jev compares agent-proposed candidates with the closest project labels and chooses an existing label or the new_label option; it does not invent the proposed label text. The backend may add only a distinct new candidate that Jev ranks in that facet's configured top N (tags: up to five; other facets: up to three) and that clears its fit, new-label and confidence thresholds. An existing-label match is reused rather than duplicated. For the associated proposal, use Jev's selected canonical existing label for that facet. Use a new candidate as a project vocabulary label only when added_to_project is true; otherwise omit it from the proposal facets and leave its evidence-backed finding intact. Treat returned jev_facet_resolutions as the recorded decision; never claim a label was added unless added_to_project is true. Label promotion requires the server's local embedding model and Jev configuration; without them the candidate remains only a review signal. Label decisions only normalize facets; they do not gate evidence assessment, proposal submission or acceptance.
If available, Jev also assesses relevance and lens in the same advisory call. Use the saved scores to focus human review; they are not evidence assessment or an instruction to silently rewrite accepted knowledge. When a reviewer decides, record the feedback and apply reusable project guidance only when the reviewer explicitly opts in. Preserve the service's exact source/extraction/location bindings. Map each factual field to evidence; assess semantic support separately. Record clear exclusions and genuinely skipped/deferred sections. Optimize for useful coverage rather than maximum note count.
Submit bounded bundles under the affected project/shared policies. Repair actionable findings without weakening evidence, changing scope to evade policy or assuming owner authority. Resume uncertain calls through status lookup. Report accepted node IDs and receipts, pending/rejected work, archive/extraction limits and actual coverage. A successful upload or schema check is not a completed ingestion.