https://analyst.nodaldata.io/mcp) exposes its tools in three connector
groups. Together they let an agent retrieve the right definitions and canonical queries for a
question and return a governed answer grounded in verified context. Every tool is scoped to your
organization and returns a JSON string.
None of these tools touch your database. They read your context and dbt repos and the Query
Hub; SQL execution stays with your own warehouse connector. See
what Nodal can access.
Business context connector
Reads your analyst-curated context repo — business domains, entities, metrics, terminology, and known data caveats.Overview of your context repo: domains, entities, metrics, terminology, and known caveats, with
files prefetched into cache. Call this first when a question involves what a term or metric means,
which domain owns something, a domain’s grain, or known data caveats.
Regex search across the cached context files.
pattern is a regex (e.g. "grain:");
file_filter is an optional glob (e.g. "*.yaml").Read a single context file, e.g.
"domains/appointments/context.md".List files and directories in the context repo. Empty
path lists the root.dbt / lineage connector
Optional. If you connect your dbt repo, these expose your models and docs so the agent can check how a metric is computed.The dbt project overview:
dbt_project.yml config, all schema.yml documentation, and the list
of SQL models and macros. Call this first to understand your data models and lineage.Regex search across cached dbt files.
pattern is a regex (e.g. "customer_id"); file_filter
is an optional glob (e.g. "*.sql").Read a single file from the dbt repo, e.g.
"models/staging/stg_customers.sql".List files and directories in the dbt repo. Empty
path lists the root.Query Hub connector
The Query Hub is your growing source of truth: verified SQL templates and resolved definitions that turn a business question into a governed answer.Semantic search over the Query Hub corpus for questions similar to the input. Returns ranked
matches with similarity scores, validated SQL templates, tables used, and filter values — use it
to discover relevant prior analyses before writing new SQL.
Extract and resolve the business entities, metrics, and temporal references in a question.
Returns resolutions with confidence, column references, and SQL expressions (including competing
resolutions where a term is ambiguous).
Turn a vague question into a fully-specified canonical question: the matched metric, parameter
resolutions (user-stated vs. defaulted), implicit filters, confidence, and any clarifications
needed. Does not return SQL — it separates clarification from execution. Returns a top-level
nodal_session_id to thread onto later calls and into commit_plan.Persist the plan and assumptions assembled for an interrogation session — an audit trail (one row
per call), joined to the session by
nodal_session_id. Optional fields include plan_markdown,
user_approved, confidence_score, clarifications_surfaced / clarifications_resolved,
dbt_models_consulted, business_context_consulted, and assumptions. Best-effort: it must
never block the user.Resource
Lists the pre-written, verified SQL templates available for your organization.