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tableau-graphql-mcp

by tdries

graphql_query

Run read-only GraphQL queries against Tableau Metadata API to explore content lineage, impact analysis, and dependencies.

Instructions

Run ANY read-only GraphQL query against the Tableau Metadata API. This is the general-purpose tool; use it for any lineage question. Returns {"data": ..., "errors": ...}.

The Metadata API is a GraphQL graph of Tableau content (workbooks, sheets, dashboards,
datasources, fields) and, with the Data Management add-on, physical assets (databases,
tables, columns) and their upstream/downstream lineage.

How to write a correct query:
- Filters are EXACT and case-sensitive: `filter: {name: "X"}` or `filter: {nameWithin: ["X","Y"]}`
  (nameWithin is the only multi/OR match; there is NO substring or regex).
- Every list field has a `<name>Connection` variant with `first`/`offset`/`after` + `pageInfo`
  (page size max 1000). Keep one query under ~20,000 nodes; narrow filters and page.
- Fields is an interface; branch with `__typename` and inline fragments
  (`... on ColumnField { columns { name } }`, `... on CalculatedField { formula }`).
- Reach a field's owning workbook via `datasource { ... on EmbeddedDatasource { workbook { name } } }`.
- `downstream*` fields (downstreamWorkbooks/Owners, external tables/columns) need Data Management
  (Tableau Catalog) and are often empty otherwise; then resolve via core lineage
  (referencedByFields -> sheets -> workbook), or just call the `where_used` tool.

Entry points include: workbooks, sheets, dashboards, publishedDatasources, embeddedDatasources,
fields, columnFields, calculatedFields, columns, databaseTables, customSQLTables, databases,
flows, tableauUsers, dataQualityWarnings. Call `lineage_examples` for ready-made query templates
and a schema cheat-sheet, or `introspect_schema` to inspect any type's exact fields.

Read-only: mutation and subscription operations are rejected. If a query exceeds the
~20,000-node limit, the response is flagged with `partial_results: true` and a `warning`
(it does NOT auto-page an arbitrary query) so you never mistake a truncated result for a
complete one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
variablesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses critical runtime behaviors: read-only enforcement, exact case-sensitive filters, page size max 1000, ~20k node limit, partial_results flag, no auto-paging, and Data Management dependency — all beyond the absent annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Long but efficiently structured with front-loaded purpose, then actionable guidelines and caveats. Every paragraph earns its place, and the organization makes the density navigable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers return format, error handling, partial results, Data Management prerequisites, entry points, and helper tools, making it fully self-contained for a complex API with no output schema shown.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema coverage, the description compensates with detailed GraphQL query construction guidance (filters, paging, inline fragments, connections). However, the optional 'variables' parameter is not explicitly explained, leaving a minor gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'Run ANY read-only GraphQL query against the Tableau Metadata API' and identifies itself as the general-purpose lineage tool, distinguishing from specialized siblings like where_used and lineage_examples.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly directs when to use it ('use it for any lineage question'), when to call where_used for downstream lineage without Data Management, and points to lineage_examples and introspect_schema for templates and schema inspection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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