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datalabs89

Tableau Public MCP Server

by datalabs89

get_twbx_lod_expressions

Parses Tableau workbook LOD expressions, generating plain-English explanations of what each calculation does and categorizing common patterns to help you understand and learn from complex formulas.

Instructions

Extracts and explains Level of Detail (LOD) expressions from a Tableau workbook. LOD expressions ({FIXED}, {INCLUDE}, {EXCLUDE}) are powerful but complex calculations. This tool parses each LOD, provides human-readable explanations of what they do, categorizes them by common patterns (percent of total, customer cohort, etc.), and includes learning resources. Ideal for understanding and learning from existing LOD calculations. Use with the mainTwbPath from unpack_twbx output.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
twbFilePathYes
includeUsageContextYes
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses that it parses each LOD, provides human-readable explanations, categorizes by common patterns, and includes learning resources. This gives a good sense of behavior without explicitly stating side effects or permissions, but the absence of any write-like behavior suggests a read-only tool.

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 three sentences long, front-loaded with the core purpose, and includes supporting details about explanations, categorization, and usage. Every sentence earns its place, with no redundant or vague phrasing.

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?

For a tool with two parameters and no output schema, the description covers the main functionality, output form (explanations, categorizations), and usage context. It stops short of detailing the exact return format, but given the absence of an output schema, it provides enough for an agent to understand the tool's purpose and typical invocation.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It provides a useful hint for twbFilePath by referencing 'mainTwbPath from unpack_twbx output', but it does not mention includeUsageContext. The schema itself has a description for includeUsageContext, but the tool description does not elaborate on it, leaving a gap for one of the two required parameters.

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 'Extracts and explains Level of Detail (LOD) expressions from a Tableau workbook', using a specific verb and resource. It distinguishes itself from sibling tools like get_twbx_calculated_fields by focusing specifically on LOD expressions.

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?

The description states it is 'Ideal for understanding and learning from existing LOD calculations' and explicitly references using 'the mainTwbPath from unpack_twbx output'. This provides clear context and a prerequisite, though it does not explicitly mention alternatives or when not to use.

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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