tableau-graphql-mcp

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where_used

Read-only

Find which Tableau workbooks and published datasources use specific columns, fields, or tables to answer impact-analysis questions and trace dependencies.

Instructions

Find which workbooks (and published datasources) USE the given names, the common 'where is this used / impact analysis' question, resolved robustly.

`names` are EXACT, case-sensitive names of any of: a Snowflake/DB column, a Tableau
field or alias, or a database table. Pass several to check them in one call. Results
group by workbook, showing how each matched (column / field / whole table, with schema
and source table) and which worksheets use it.

This uses CORE lineage (referencedByFields -> field.sheets -> workbook, and
field.datasource -> workbook), so it works even without the Data Management add-on where
`downstreamWorkbooks` is empty. For other shapes of question, use `graphql_query`.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "names": "<names>"
    },
    "name": "where_used"
  }
}

Input Schema

Name	Required	Description	Default
`names`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 7, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds real behavioral context beyond that: it explains the underlying CORE lineage path (referencedByFields -> field.sheets -> workbook) and notes it works even without the Data Management add-on where downstreamWorkbooks is empty, which is non-obvious operational detail.

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

Conciseness4/5

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

Front-loaded with the core answer, then parameters, then mechanism, then alternative routing. The lineage explanation sentence is dense but earns its place by justifying the tool's robustness claim; slightly longer than strictly needed.

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

Completeness4/5

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

An output schema exists, so return values need not be spelled out, and the description still sketches result grouping (by workbook, match type, worksheets) for orientation. Combined with the parameter detail and alternative routing, an agent has what it needs to invoke it correctly.

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

Parameters5/5

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

Schema coverage is 0%, so the description carries the full burden and does so: `names` are EXACT, case-sensitive, and may be a Snowflake/DB column, a Tableau field or alias, or a database table, with several accepted in one call. This fully specifies input semantics the bare array schema omits.

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

Purpose4/5

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

States a specific verb+resource ('Find which workbooks and published datasources USE the given names') and frames the exact question it answers. It names graphql_query as the escape hatch for other question shapes, but does not differentiate itself from the sibling impact_analysis, which covers an adjacent-sounding 'impact analysis' question.

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

Usage Guidelines4/5

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

Explicitly says when to use it (the 'where is this used' question), that multiple names can be checked in one call, and redirects other question shapes to graphql_query. No exclusion is drawn against impact_analysis, so the routing advice is clear but not exhaustive.

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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