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tdries

tableau-graphql-mcp

by tdries

where_used

Find which Tableau workbooks and published datasources use specific columns, fields, or tables. Run impact analysis by passing exact names to identify dependent worksheets and data sources.

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`.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description carries the full burden and excels: it discloses exact case-sensitive matching, grouping of results by workbook, and the underlying core lineage mechanism, setting clear expectations for the agent.

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?

The description is concise yet dense, with each sentence serving a distinct purpose: purpose, parameter details, result behavior, and alternative tool. There is no waste or redundancy, and important details are front-loaded.

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?

Given that an output schema exists, the description doesn't need to explain return values. It fully covers the use case, parameter semantics, behavioral nuances, and alternatives, making it complete for a query tool with minimal parameters.

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 description coverage is 0%, but the description thoroughly explains the `names` parameter: what types of names are accepted (column, field, alias, table), exact and case-sensitive matching, and support for multiple names in one call. This fully compensates for the lack of schema-level parameter descriptions.

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?

The description clearly states the tool finds workbooks and published datasources that use given names, with a specific verb and resource. It explicitly names graphql_query as an alternative for other question shapes, distinguishing from at least that sibling.

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?

It provides explicit context for when to use this tool for 'where is this used / impact analysis' questions and says to use graphql_query for other shapes. It also explains that it works without the Data Management add-on, giving clear usage exclusions and conditions.

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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