- `node_type` narrows to one type: PDS (published data source), WORKBOOK, SHEET, DASHBOARD,
  EMBEDDED_DATASOURCE, TABLE, FIELD, WAREHOUSE_TABLE, WAREHOUSE_COLUMN, CONNECTION, SCHEMA,
  SITE, SEMANTIC_CONTEXT, SEMANTIC_CONTEXT_EXTERNAL.
- `scope_id` limits the search to one data source's subtree (a PDS node id plus its tables
  and fields).
- `limit` 1-100 (default 10); `threshold` drops matches scoring below it (0-1).

Use the returned `id`s with get_node, list_edges, lineage and impact_analysis.

`node` is a node id, or a plain-language description that is resolved by semantic search
(the response then includes `resolution` with the match score and alternatives). Set
`include_children` false to skip `connected_nodes` on large containers.

`node` is a node id or a plain-language description. `edge_type` narrows to one of: CONTAINS,
HAS, JOINS, DEPENDS_ON, LINEAGE, SEMANTIC_EQUIV, DESCRIBES. `direction` is "outgoing" or
"incoming"; omit for both.

Containment (a table HAS a field, a workbook CONTAINS a sheet) is not followed, so a single
field often returns only itself; use list_edges or get_node to see what contains it.

`node` is a node id or a plain-language description. Returns the anchor node, `nodes`,
`edges` (source_id -> target_id, with type), and counts by node and edge type. For "what
breaks if this changes?", impact_analysis is more direct.

`node` is a node id or a plain-language description. Returns `total`, `counts_by_type`, and
`affected_assets` sorted by type then name.

- With `node` (an id or a description): only the contexts attached to that node.
- Without `node`: every context on the graph; `is_global` true = only graph-wide contexts,
  false = only node-attached ones, omitted = both.

The API doesn't filter by text yet; to find statements by meaning, use search_nodes.

`pds_id` scopes the report to one published data source's subtree. `severity` ("high",
"medium", "low"), `suggestion_type` and `limit` (default 20; a busy graph has hundreds) filter
only the suggestion lists; the score, metrics and `summary` totals always cover the full (or
PDS-scoped) graph.

Set exactly ONE of: `target_node_id` (an exact node id from search_nodes/get_node; the
context then DESCRIBES that node) or `is_global=true` (applies to the whole graph).
CONNECTION, SITE, SCHEMA and semantic-context nodes can't be targets. Some sites accept only
graph-wide context through the API (error node_semantic_context_disabled): then use
is_global=true and name the field or data source in each statement. `name` is optional
(derived when omitted). Needs the Creator role. Returns the new context, with its id.

- `statements` REPLACES the whole stored set (include every statement you want to keep).
- `name` renames it.
- `attach_to` (an exact node id) re-attaches it to that node; `make_global=true` detaches it
  and makes it graph-wide. Not both.

Tableau-managed (SEMANTIC_CONTEXT_EXTERNAL) contexts can't be changed. Needs the Creator role.

Returns what was deleted (name and statements, so it can be recreated if needed). Deleting an
id that doesn't exist succeeds without effect. Needs the Creator role.

`datasource` is a PDS id (from list_sources) or a description of it. Logical tables become
datasets, fields become fields, row-level calcs become fields and aggregate calcs become
metrics; every calc keeps its Tableau formula and gains portable SQL when the translation is
exact. Semantic-context statements become ai_context. `default_aggregation_metrics` also turns
each measure's Tableau default aggregation into a metric (total_sales = SUM(sales)).
Always read `losses`: Tableau Knowledge has no physical table paths or join keys, so `source`
and `relationships` need filling in before the model is used elsewhere. See ossie_guide.

`document` is the model as YAML or JSON text. Elements are matched to nodes of one published
data source: models exported by export_to_ossie map back by their stored ids; other models
need `target_datasource_id` (an exact PDS id from list_sources) and match by name. Each matched
node gets one context named `ossie:<model>` holding its description, ai_context instructions,
synonyms ("Also known as: ..."), example questions, metric definition (`include_definitions`)
and relationships. Re-importing updates those contexts instead of duplicating them.

`scope`: "auto" (default) attaches contexts to nodes and, on sites that accept only graph-wide
context through the API (error node_semantic_context_disabled), writes the plan's
`global_fallback` instead: one global `ossie:<model>` context whose statements are prefixed
with what they describe. "node" never falls back; "global" plans the global context directly.

Workflow: call without `apply`, show the user the operations (create/update/unchanged) and
`unmatched` list, then call again with `apply=true` once they agree. Needs the Creator role.

Checks: the "# " line format (a slip makes Tableau silently ignore the line), the limits (Tableau
Cloud: 50 rules and 8,000 characters per dashboard; Tableau Next: 30,000 per semantic model),
headers, duplicates, contradictions, thresholds without "use only this value", formatting rules
that would leak into queries, conversational lines, absences that can be over-read, ambiguous
ordering and averages, and missing baseline rules (query formatting, failure handling).

`target` (a dashboard, sheet, workbook or data source: id or description) also checks every
[Field] and "Field" the file names against the captions the agent will see in Tableau Knowledge,
with "did you mean" suggestions, and flags workbook problems (duplicate, generic or helper field
names). `product` is "cloud" or "next". Returns `ok` (no errors), `issues` by line, and stats.